climate change in manipur essay

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Impact of climate change on the Manipur ecosystem: an overview

Climate change is a global phenomenon and one of the burning issues in the present scenario. This article provides a description of the impacts of climate change on Manipur's agricultural, woodland, and wetland ecosystems; a north-eastern Indian state. Some major findings in the correlation with the effects of climate change on different ecosystems have been discussed however the more observations in the context of climate change are still to be inextricably linked. The impacts addressed in the area are rare and in many cases do not require a common methodology and standard instrumentation and only based upon the literature survey by preexisted researches done on the regard of climatic changes and its influence in Manipur ecosystem. The impacts of climate change were also classified based on the most recent different climatic elements, such as heavy rains, temp, CO2 concentration, etc. Also there is need to interface with some other potential researchers in concern subject to ma...

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Dr Thounaojam Thomas Meetei

Soil health is one of the most important resources for sustainable agriculture, improving agro-ecosystems and environmental sustainability. Improving soil health is the only key factor for safeguarding our future food grain production. Since time immemorial everyone is busy for the production of food only without taking care of the soil. Nowadays people are more aware of good soil health after the introduction of the concept ‘sustainable agriculture’. With sustainable agriculture, one will be producing foods without compromising future production. There are certain management practices for improving soil health. Among all agroforestry system is one of the most important that can improve the soil health with safeguarding the future production. With the introduction of agroforestry, different soil physical, chemical and biological properties increase with improving the farm incomes. Agroforestry is the combined venture of forestry, horticulture, agriculture and vetenarary. With the in...

editor of J E T I R Research journal

Land use and land cover is an important component in understanding the interaction of the human activities with the environment. Mapping land use/land cover (LULC) changes at regional scales is essential for a wide range of applications, including landslides, erosion, land planning, global warming etc. LULC alteration (based specially on human activities), negatively affect the patterns of climate, the patterns of natural hazards and socioeconomic dynamics in global and local scale. The Lankamalla reserved forest is dense forest in the surroundings of kadapa and its geographical extent is 498.67km 2. In recent times, remote sensing and geographical information system (GIS) is extremely beneficial in assessing the land use land cover data analysis. Remote sensing is considered as a significant data source for forest monitoring purposes, and has been widely used for monitoring deforestation. LANDSAT 8(OLI) of 2014 and 2018 were downloaded from USGS Earth Explorer, for analyzing the trends in deforestation and land use land cover changes in Lankamalla forest and its neighbourhood. In image preprocessing, supervised classification has performed to classify the images into various land use classes using Digital Image Processing tools. The study area is classified in to 1. Dense forest, 2. Deciduous forest, 3. Agriculture land, 4. Water bodies and 5. Waste land. The Agriculture land is again sub dived into three classes a. Crop land, b. Fallow land and c. Plantation. The results indicates that during the last decade dense forest of the Lankamalla reserved forest area have been decreased about 46.55% (i.e, 223.86 km 2) and 27.21% (i.e, 130.86km 2) while Water bodies, Crop land decreased by 0.008%, 0.034% respectively. The study area consists of ten mandals, such as Atlur, Badvel, Bhramamgarimattam, Chennur, Kadapa, Khajipet, Sidhout, Mydukur, Vallur and Vontimitta. In the Sidhout area dense forest, Fallow land, Crop land, Plantation, Waste land have increased. The crop land increased from1.2% to 5.95%, fallow land increases from 10.93 to 23.01%, plantation increases from 2.8 to 20.04%, and waste land occupancy increases from 7.85 to 20.85 %.

Journal of Emerging Technologies and Innovative Research (JETIR),ISSN No :-2349-5162

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Biophysical characterisation and phenological analysis in heterogeneous forests is a challenging effort due to highly complex and biodiverse tropical forest ecosystems. Apart from the traditional and multispectral remote sensing technologies like Landsat imageries, MODIS etc, high resolution Sentinel imagery can contribute significantly in understanding the tropical forest ecosystems phenology, global carbon cycle, productivity of the forests and carbon budgets. Here we propose a methodological approach by the integration of space borne LiDAR data with the Sentinel-2 imagery for the accurate estimation of the biophysical parameters and understanding the phenological patterns in the Western Ghats of India. The structural parameters estimated from the LiDAR data and the spectral parameters from the Sentinel-2 imagery are integrated, and the resultant obtained is processed by means of nonparametric regression-based Support vector machine algorithm. The results obtained are validated and are found to have strong correlation with ground measurements. Additional vegetation parameters because of the spectral enrichment and dense time series Sentinel-2 imagery contribute in understanding significant information of forests including degradation, health status and the complex ecosystems. The study also has the advantage of understanding the understory vegetation conditions of the forests ecosystem which contribute an important help for forests management and practices.

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Ahmed Nisha

Agriculture expansion has resulted in the loss and degradation of native forests, which adversely impact the livelihoods of the marginalized and forest dependent communities. It also results in the loss of ecology, ecosystem services and economy of the region. Widespread loss of natural habitats in the tropics has led to increased interest in production landscapes such as agroforestry for biodiversity conservation. Thus this review paper attempted to explain the beneficial impacts by opting coffee agroforestry for tribal welfare and forest conservation. Shade grown coffee helps in conserving biological diversity by providing several ecosystem services such as erosion control and water recharge, thereby preventing the degradation and loss of surrounding habitat. It also provides livelihood and also increases women empowerment among tribal people. Hence, Coffee plantations should be considered as a complement to other conservation efforts for biodiversity conservation, as well as for sustainable management of resources.

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India: In rural Manipur, women feel the heat of climate change

By Ninglun Hanghai

Women farmers in Manipur are struggling to grow crops in an increasingly hostile environment in north-east India

It is that time of the year when the weather is dry and windy. Hmuoki has to work even harder than usual to water and fertilise her four acres of farmland on the banks of the Khuga River in Churachandpur district of Manipur, north-east India.

Hmuoki struggles to ensure her family has enough food to eat during the dry season, which begins in November. From November till March, she plants crops like mustard, pumpkin, varieties of peas, beans, ginger and turmeric. She also supplements her food by working on farms in the hills surrounding the village, by going to Jhum fields or to collect wild fruits or herbs.

Women like Hmuoki play a major role in agriculture in Manipur. There are a large number of women farmers working in the state, suffering from protracted conflictbetween authorities and armed groups. These women are also on the front line of climate change in the north-eastern state, where erratic rainfall, floods and higher temperatures are making daily life more difficult for rural farmers.

In mid February, a small group of women farmers gathered at Hmuoki’s home with a team from the civil society organisation Rural Women Upliftment Society (RWUS) to talk about their experiences in the fields and the changes they have witnessed in recent years. Hmuoki lives in Saidan village, 5 kilmetres from Lamka, the main town in the district.

February is the time when women prepare for Jhum cultivation – a traditional slash and burn agriculture technique used widely in the hills of Manipur. The women are also seeing more random forest fires, set off by people who want to hunt wild animals. This causes huge damage to the land. Burning for Jhum preparation, however, does not damage soil fertility or cause environment degradation, argued the women. For Jhum preparation a small forest area is cleared, and twigs, branches and waste from the clearings are dried and eventually burned. The ash actually helps the make the soil more fertile.

Forest fires also affect their water sources – natural springs, which are slowly shrinking and drying up.

For many years now certain areas in the forest surrounding the village have been declared a “forest reserve” by the village authority (under the Chief) to preserve its resources, mostly for fuel and wood. “Due to this we had to even go farther for our Jhum cultivation,” said Thiengi, a woman who dropped in to the meeting on her way to the Jhum fields. “Now our Jhum cultivation field is very far from our village,” she said. There is no time to rest, as she goes to Jhum fields about an hour’s walk away, to get herbs and other edible leaves and plants.

Too little, too much water

The women dread the summer when water will be really scarce. Saidan village, with over 100 households, has three public water reservoirs, but the women say this is insufficient. Rainfall, the main source of water for domestic use, has become sporadic and this sometimes leads to drought.

The women have also seen more frequent floods over the past decade. Floods become a major hazard during the monsoon. “My field/ farm was frequently destroyed by flood, damaging all crops in monsoon,” said Hmuoki. This is one reason why she works persistently during the winter months, struggling hard to keep her farm in good condition, so as to extend the crop-growing season.

Even if there is no heavy rain, water released from behind the Khuga dam sometimes inundates the whole river downstream, damaging crops, and houses around the river. Khuga Dam is a multipurpose hydroelectric project located in south Churachandpur. During the monsoon, water is released from behind the dam to relieve pressure. The dam was completed in 2010.

Water for domestic use and for agriculture and irrigation is a key concern. The burden of collecting water falls on women. Besides the drying of springs, one of the major causes of water scarcity is the lack of snowfall. “Ten or twenty years back we had snowfall in winter. There is no snowfall now,” explained Hmuoki. “Snowmelt makes the soil wet and fertile”.

Therefore women farmers like Hmuoki have to put even more effort into raising their crops, which is their main livelihood. At the moment, her farm is on the banks of the Khuga River, which provides irrigation. But Hmuoki is worried. The Lanva, a tributary of the Khuga that runs through Lamka town, has completely dried up during winter – though it still floods causing havoc during the monsoon.

Feeling the heat

The summer months are increasingly hot, said the women from Saidan village. February, once upon a time a cool, breezy month, was already warm and sultry this year. Though rural villages in Churachandpur have to bear the summer heat without amenities, in Lamka ceiling fans and standing fans have become a necessity. In peak summer, temperatures in Churachandpur have reached over 30 degrees Celsius in recent years. Record temperatures in Manipur reached 35.6 degrees Celsius in April 2014. Many families in Manipur have also fitted air conditioners at home. These changes have been distinct in the past few years, compared to the usually pleasant and moderate climatic conditions in Manipur.

Rising temperatures have made it more difficult to preserve seeds. Traditionally, seeds were preserved by drying them out in the sun or above fireplaces and keeping them in bamboo containers. In past generations, seeds would be planted directly in the soil. Now seeds have to be germinated first with manure and water in nurseries at home before being planted in the soil, to ensure crops grow well.

The hard work doesn’t end there. Once the crops grow, they come under attack from a growing number of insects and other pests. “These days we buy and use pesticides and fertilizers, our homemade manure is not sufficient,” said Hmuoki.

It’s not only crops. Mangoes, one of the common fruit plants in Churachandpur, for instance, are vulnerable to infection, said Thiengi. “Crops like peas are impossible to grow now without insecticides and pesticides,” she said.

Plants and fruits that were once delicacies for the locals have slowly disappeared. One example is the zawngtah or “stink bean” (Parkia speciosa). The bean trees have slowly dried up and been infected by pests and the price of the beans has gone up in recent years. The plant used to grow in the wild as well as being cultivated. Now the beans have to be imported from Moreh, a town and trade hub 270 kilometres away from Lamka on the Indian-Myanmar border.

Uphill struggle

Farming has become a real struggle for these women in Manipur. They want to take advantage of recent developments in technology and modern agricultural techniques to boost their productivity and experiment new ways of farming. They also hope to find alternative ways to earn income. For instance, introducing new varieties of climate-resistant seeds, or new varieties of crops, such as pulses.

It is rural women, particularly farmers, who see and experience climate change, said Mary Beth Sanate, secretary of the Rural Women Upliftment Society. They are the ones who are preserving natural resources, but unfortunately, policymakers do not consider their knowledge and skills. “Women are not part of the decision making bodies, be it at the local level, or in traditional or state bodies,” said Sanate.

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Climate Change In Manipur In Context Of COP 26 Of UNFCCC

Resolution of the consultation on climate change in manipur in context of cop 26 of unfccc.

The participants of the consultation on “Climate Change in Manipur in context of COP 26 of UNFCCC”, organized by the Centre for Research and Advocacy, Manipur and the Youth Forum for Human Rights in Manipur at the Manipur Press Club on 8 November 2021, hereby:

Express deep concern with the worsening Climate Change impacts in Manipur: viz, record high temperatures, unpredictable flood, drought, diseases, crop failure, drying of water sources, biodiversity loss etc, that unleashed suffering, livelihood loss, displacement among indigenous communities.

Noted the Inter-Governmental Panel on Climate Change report of 2021 that reported alarming impacts of climate change, rapid temperature rise, increased in disasters, biodiversity loss, rapid sea level rises etc.

Alarmed with unsustainable development processes in Manipur amidst India’s neoliberal development targeting peoples’ land, forest, water and resources. Mega dams, mining, oil exploration, roads, railways, transmission lines pursued as part of India’s Act East Policy worsened land loss and climate change.

Express concern that large dams such as 66 MW Loktak Downstream Project, 190 MW Pabram Dam, 70 MW Nungleiband dam etc will destroy forest, agriculture land and threaten indigenous peoples livelihood, culture and identity while deepening climate crisis and impacts on indigenous peoples’ rights.

Concerned with increased land grabbing in mega infrastructure projects, Trans Asian Railway and Asian Highway projects, Imphal Town Ring Road etc, that will cause destroy agriculture land and forest areas.

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Considered with increased financing by the Asian Development Bank, World Bank, JICA etc in energy, infrastructure project with climate change and human rights implications.

Concerned with the push for palm oil and large dams in Manipur to increase forest cover and as renewable energy as part of India’s Intended Nationally Determined Contributions (INDC) to the UNFCCC.

Condemn the false climate change solutions pursued in Manipur and efforts of corporate bodies trying to seek profit by plundering natural resources of Manipur. The National Hydroelectric Power Corporation tried to seek carbon credits and profits from its controversial 105 MW Loktak Hydroelectric Project in Manipur from the CDM of UNFCCC despite violations.

Concerned that countries used the COP26 of UNFCCC to craft neoliberal policies through false climate solutions like Net Zero Emissions, carbon trades, carbon sequestration etc to respond to climate crisis. The COP 26 will reinforce business as usual for corporations to profit by exploiting nature and people.

Hereby Resolved that

1) The Government of India, corporations and international financial institutions should protect and promote indigenous peoples self-determined rights over their land and resources and should take their free, prior and informed consent before targeting their land and resources.

2) All MoUs for mega dams, oil exploration, mining, agri-business (viz. Palm Oil) pursued without their consent should be revoked for possible adverse impacts on environment, people and climate in Manipur. The MOUs granted to Roukela Private Ltd, Sarvesh Refractories, Gulf Natural Resources for mining and to Jubilant Oil and Gas Pvt Ltd and to Oil India Limited for oil exploration in Manipur should be revoked.

3) All neoliberal policies, such as Manipur Hydro Power Policy, 2012, North East hydrocarbon vision 2030, Manipur Loktak Lake Protection Act, 2006, the Mining and Minerals (Development and Regulation) Amendment Bill 2015 etc that facilitated the plunder of peoples’ land and resources should be repealed.

4) Dam building companies, viz, National Hydroelectric Power Corporation (NHPC) should stop building 66 MW Loktak Downstream Hydroelectric project and other dams that will destroy forest, agriculture land, river and peoples’ livelihood. All MoUs with NHPC and NEEPCO for dams should be revoked.

5) The Government should review and rescind its plan to promote palm oil in Manipur and North East given its documented adverse impacts on people, environment and climate in Mizoram, Indonesia, Malaysia etc.

6) The government should stop counting mega dams and palm oil as renewable energy and as solutions for climate change, in its effort to fulfil its INDC.

7) International Financial Institutions and corporates should stop financing unsustainable development projects and false climate solutions with social, environment and climate impacts. Climate finance should desist funding social and environmentally damaging projects.

8) We reject all false climate change solutions that violate indigenous peoples’ human right and their self-determination over their land and resources.

9) All climate change mitigation and adaptation related policies should ensure compliance with indigenous peoples’ rights as per UN Declaration on Indigenous Peoples Rights, 2007.

10) COP 26 of UNFCCC should desist from any decisions that serve corporate bodies interest for profiting by plundering peoples land and resources.

11) Indigenous peoples’ traditional sustainable management of land and natural resources, including role of women, youths etc and their low consumption and carbon oriented way of life must be promoted in all development decision making processes that can affect their land, lives and future.

12) The Manipur State Action Plan on Climate Change and other climate policies should be reviewed to ensure its compliance with human rights and sustainable development goals in its implementation.#

References: – Resolution: Climate Change in Manipur in Context of COP 26 of UNFCCC – Consultation on Climate Change in Manipur in Context of COP 26 – Revoke MoUs for mega dams, oil exploration, mining, agri-business in Manipur

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Manipur People are missing out on the Larger Climate Change Debate

Manipur can tackle climate change by protecting indigenous rights

Residents living in Manipur’s Reserved Forest (RF) and Protected Forest (PF) areas have been served eviction and show-cause notices since 2016. The Kuki tribes who had settled in K. Songjang village in Churachandpur were evicted from their villages.

The government argued that several such households had been illegally encroaching on the RF and PF areas, and that the state must initiate this legal eviction procedure to safeguard the forest amid climate change and water scarcity issues in Manipur. Since 2016, several households have been evicted from RF areas.

About 84 households were evicted from Heigang RF in 2016; 74 were evicted from Awangching RF in 2018; 16 were evicted from Langol RF in 2019; and about 83 households from Waithou RF, according to RK Amarjti Singh, Conservator, Central Forest Circle, Manipur.

Also Read | Understanding the ongoing crisis in some areas of Manipur

Singh claims that Manipur only has 4.4 per cent RF, 14.4 per cent PF, and 54.81 per cent Unclassified Forest, most of which is concentrated in the State’s hilly regions. This eviction drive is only taking place in the RF and PF areas. He stated that the eviction drive excludes the Unclassified Forest area. About 984 sq km of RF, 3254 sq km of PF and 13180 sq km of unclassified forest are found in Manipur.

The total Recorded Forest Area (RFA) is 17418, or 78.01 per cent, of the State’s overall 22327 sq km of land ( Forest Survey of India , 2021).

Singh urges the public to conserve RF and PF because their percentages are lower than those of Unclassified Forest, which keeps the ecological health of Manipur in balance (Amarjit, 2023). There are currently about 39 reserved forests in Manipur, including wildlife sanctuaries and national parks.

Also Read | Northeast needs a lot of things: Hindi is not one of them

Response to eviction notice

On 20 February 2023, the residents of K. Songjang village were served with an eviction notice for the construction of a house along the Churachandpur-Khoupum PF stretch in Churachandpur. Most households, about 14 homes, were mapped out to have been built after 2021. So, these households were deemed illegal and allegedly received eviction notices .

Subsequently, the village chief was given notice of the legal process before they were bulldozed to destruction. Another show-cause notice was issued for Kungpinaosen, Kangvai Sub-Division, Churachandpur District, Manipur.

The villagers were notified to leave for the same reason as K. Songpijang. However, the Kuki People’s Alliance claimed in a statement released on 7 March 2023 that Kungpinaosen village, including its offshoot five villages, namely K. Songjang, K. Hengmol, K. Lhangnom, New Kungpi Part-1, and New Kungpi Part-2 villages, are not included in the area that has been demarcated as Churachandpur-Khoupum PF since the FSO proclamation dated October 29, 1971.

The organisation also claimed that the villagers had every right to reside in K. Sojang village because it had been there since before the Indian Forest Act of 1927 was put into effect.

Considering this issue, the All Tribal Student Union of Manipur (ATSUM) released a statement on 21 February 2023, describing the hillside eviction drive cruel and inhuman as it curtailed the means of subsistence for the people who depended on the forest. They also assert that the government violated the correct procedures when declaring certain forest areas as reserved, particularly in the Hill Areas, as a procedural error. In resolution No. 38/2020, passed on 11.0.2021, the Manipur Legislative Assembly (MLA) Hill Area Committee (HAC) noticed that the declaration of RF after 1972 was invalid. It maintained that any declaration of RF, PF, or wildlife sanctuaries/national parks made on or after 20 June 1972 must first receive approval from the HAC regarding Scheduled Matters of Article 371-C of the presidential order of 1972.

Also Read | Manipur NRC: History shows demands are easy to make, impossible to fulfil

Emerging debates

Recent eviction orders and the resistance by the affected people, and protests by the Chin-Kuki civil society organisations have sparked a number of new discussions.

Currently, the discussion is centred on topics like immigration, forest conservation, increasing villages in the hills, and abnormal population growth in the Hill Areas.

The larger discussion is concentrated on climate change and has decriminalised the Hill Tribes of Manipur for engaging in activities like illegal poppy and timber trades, among other things, in many news channel discussions like Impact Tv Manipur and others state newspapers.

According to the general view of the populace, these actions have ultimately contributed to water scarcity and deforestation in the Manipur hills and valley.

As a result, there is a demand for the state government to take over the management of the forest resources, particularly in the hill areas, and impose strict laws, rules and regulations. In this scenario, what is affecting Manipur’s climate change is still questionable. Is it only the deforestation in Manipur’s hilly areas damaging the ecosystem? What about development infrastructure projects which lack social and environmental impact assessments? What about environmental pollution brought on by anthropogenic human activity in Manipur? It is essential to talk about the crucial aspects of Manipur’s changing climate alongside external forces contributing to deforestation in the hilly areas.

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Larger discourse on climate change

Colonialism and capitalism have historically facilitated industrialisation, militarisation, and the development of a carbon-intensive economy, all of which have contributed to anthropogenic climate change. The centuries-long colonial invasion “caused anthropogenic environmental changes that rapidly disrupted many Indigenous Peoples, including deforestation, pollution, modification of soil-use amplification and terraforming for specific types of farming, grazing, transportation, and residential, commercial, and government infrastructure” (Whyte, 2017). In other words, rather than being a recent phenomenon, vulnerability to climate change continues the colonisation process.

Guha and Gadgil (1989) argued similarly for the Indian context, claiming that the colonial foresters’ or departments’ strategy in India was to exploit the forest through timber extraction and generate income from it under the guise of forest conservation. Many Indians, particularly the tribal groups, have resisted colonial attempts to control their lands, forests, and water resources. The resistance movement in the post-colonial era still continues.

Neoliberalism’s emergence in Indian post-1991, particularly in Northeast India, gave focus to the country’s “Look East” policy. Since then, tribal communities in India have protested against the Indian government’s numerous laws that support the mining industry and capitalist forms of projects which seek to alienate tribal communities in the name of development and national interest.

For instance, the Oil Refinery movement in Assam (Baruah, 2011); the tribal movement against Uranium mining in Meghalaya; and the struggle against big dam construction in Arunachal Pradesh, Sikkim, and Manipur and so forth are some environmental and social movements undertaken by tribals in Northeast. Resources have been privatised, and wealth has been disproportionately amassed through profit and the appropriation of land and resources from people whose livelihoods depend on land and forests for industrialisation, eco-tourism, and the extraction of oil and minerals (Yumnam, 2021; Kikon, 2019). These activities and factors have sped up climate change in Northeast India, especially in Manipur.

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Contributing factors to climate change

In the context of Manipur, the more considerable discussion on climate change and the factors influencing it needs deliberation. The general narrative about poppy cultivation causing deforestation should not invisibilise the larger discourse on climate change in Manipur. Indeed illegal poppy cultivation has its share of implications for the local ecosystem, and it needs to be stopped immediately.

The initiative on the ” War against drugs” and “Chief Minister’s Manipur Green Mission” campaign initiated by the Government of Manipur is both necessary and commendable. But Government of Manipur should also focus on the adverse effects of more significant development initiatives taking place in the State. The ineffective dams in Manipur are still unproductive but have harmed the environment to a great extent (Langhu, 2019; Yumnam, 2021). Various developmental projects under the Act East Policy (AEP) have been implemented. For instance, constructing trilateral international highways between the three nations, India, Myanmar, and Thailand, with Manipur as their gateway point and the Imphal-Jiribam railway project in India, are matters of more significant worry, environmentally speaking.

Moreover, numerous Memorandums of Understanding (MoUs) have been signed between the Manipur State and business corporations that support the extractive industries through the Public-Private Partnership (PPP) model under AEP. These projects will undoubtedly alter the local ecological system, which should be of more significant concern (Yumnam, 2021).

Once regarded as a frontier with rugged mountainous terrain, the NER is now seen as the gateway to Southeast Asian countries (Karlsson, 2011; Wouters, 2020); under pressure, progressive development projects are being pushed together with the implementation of AEP. Large forest tracts are being acquired and destroyed to build infrastructure, extract resources, construct the Imphal ring roads, and consider building concrete dams (Yumnam, 2021). More severe threats include rapid urbanisation, market demands for timber from plywood production companies, firewood from brick factories and military bases, etc., and charcoal from restaurants and hotels. Additionally, connectivity and the development of modern machinery like handsaws, sawmills, and trucks have significantly accelerated deforestation (Langhu, 2020). The more significant threat to nature comes from unrestrained, capitalist-driven exploitation of forest resources for profit, which needs particular focus.

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The introduction of the Indian Forest Act in 1878 and 1927 converted all communal forests into state property and destroyed forest-covered areas. For instance, the colonial authorities cleared a substantial forest area for constructing the Imphal-Cachar and Imphal-Dimapur highways. The historical examination of the Colonial forest laws in India and Manipur has distinctly looked at the exploitation and destruction of forests to generate profit and privatised and commercialised forest resources. The colonial laws have legitimate capitalist forms of power to acquire the land and forest resources for extractive mining activities at present is a significant concern. For instance, the Land Acquisition Act of 1894 and 2013 has permitted the State to acquire land for any public purpose or a company under the Eminent domain doctrine.

Many MoU has been signed between the Government of Manipur and the corporates for extracting oil resources, palm oil and developmental projects that require significant consideration before commencing. The Government of Manipur should review the MoUs signed with the corporates or companies for extracting minerals resources, including Jubilant Energy’s Plan for oil exploration, limestone mining, and chromite mining in Ukhrul (Yumnam, 2021) etc., taking due cognisance of these issues and challenges.

The state should focus more on providing alternative means of subsistence to discourage the hill people from continuing poppy cultivation in the hills. The market needs constant monitoring to check illegal drug trafficking and manufacturing.

Education, employment opportunities, ensuring inclusive infrastructure development in the hills and valleys, and other livelihoods that sustain the ecosystems, among other things, are becoming more crucial when increasing the unemployment of educated youths in the State. Such government initiatives will only end the vicious cycle that the Hill Areas’ inhabitants are caught in and might reduce the poppy cultivation in the hills.

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The world’s events and disasters have taught us that nature has its way of preserving its equilibrium. Such issues and problems are not always resolved by human beings blaming and providing illogical justifications. It is noteworthy that the Hills have long protected the State’s forested areas, maintaining the State’s ecological health.

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The Hill people effectively manage shared resources like community land, water, and forests because of their deeply ingrained values and still-viable cultural practices. Because the assets are inherited from their ancestors, these communal resources such as land could only be transferred to close kin and relatives.

The forest is shielded from exploitation by capitalists who view the forested areas as a commodity and a way to amass wealth through these customs and traditional knowledge systems. The only way the tribal communities in Manipur can lessen the impact of climate change is by adhering to the cultural norms and practices of communal resource management, which Article 371-C of the Indian constitution also protects.

Onhring Langhu is a research scholar at the Tata Institute of Social Science in Mumbai. Views expressed by the author are personal and do not reflect EastMojo’s stand.

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Climate Change

Climate crisis in north east india: how geography, rainfall variations define calamity course.

DTE analyses geography of 8 northeastern states and shows what changing climate and rainfall patterns mean for the region

By Akshit Sangomla

Published: monday 06 september 2021.

In the first part of the series tracking climate change in North East India, Down to Earth traced changing rainfall patterns over the region in the last century that have resulted in its overall drying up.

The North East India, which normally receives heavy rainfall during the monsoon months (June-September), has changed character for the worse. The flood-drought cycle now has begun to happen within a year, especially during the monsoon. The rains come in quick bursts and flood the region, followed by elongated dry periods that border on drought .

DTE analyses the geography of the eight northeastern states and what changing climate and rainfall patterns have meant for the region.

The state is divided into three distinct geographical regions. To the north is the Brahmaputra river valley at the foothills of the Himalayas; to the south is the Barak river valley. In between the two valleys are Karbi Anglong and Cachar hills.

Monsoon rainfall in the state showed a declining trend between 1989 and 2018. Several years in the last two decades had deficit rainfall, below the long period average (LPA) of 1,486 millimetres.

The state receives its maximum rainfall in July, with 21 per cent variability in rainfall in the last 30 years. Overall monsoon rainfall variation in the period was close to 13 per cent.

A district-level analysis presented a different scenario, where the dry drought-like districts were intermixed with floods due to rainfall in other districts. Most districts to the north of the Brahmaputra, such as Barpeta, Sonitpur, Lakhimpur and Nalbari, showed an increasing trend in rainfall, according to data by the India Meteorological Department (IMD).

These districts also host all tributaries of the Brahmaputra that flow down from the high Himalayan mountains in Arunachal Pradesh to join the Brahmaputra. The change in geography from the mountains to the plains is abrupt and steep, which makes the region naturally prone to flooding as well.

An increase in rainfall means tributaries get flooded more often, thereby increasing the chances of floods in these districts and those in the south of the Brahmaputra, where the rainfall trends were on the decline.

The number of rainy days decreased in most districts to the north of the Brahmaputra. This means that increased rainfall now happens over fewer days, increasing chances of river flooding.

In several districts to the south of the Brahmaputra, tributaries come in from other states where flooding mostly happens due to heavy rainfall elsewhere. Golaghat district — home to the Kaziranga National Park famous for one-horned rhino, Bengal Tiger and Asiatic elephant — often gets flooded due to the Doyang river that flows in from Nagaland’s Wokha district.

Arunachal Pradesh

The state has a significant forest cover of 82 per cent. It is mostly mountainous, and the only plains are in a thin band of land at the foothills bordering Assam.

The forests cover the high hills in the south; snow covers the Himalayas to the north. All major tributaries of the Brahmaputra — like the Siang, Dibang, Subansiri and Lohit — come down from the mountains and fall into the plains of Assam at a steep gradient, making them prone to flooding.

Between 1989 and 2018, monsoon rainfall showed a significant decreasing trend, with many years experiencing deficit rainfall below the LPA for monsoon period of 1,726 mm.

The state received its maximum rainfall in July when it showed a high variability of 31 per cent. The overall variability of monsoon rainfall for the period was also much higher than for Assam: 21.7 per cent.

The analysis presented a stark contrast among districts with two — Upper Siang and Upper Subansiri — showing a significant increase in rainfall for the period. The Siang, which is the biggest tributary of the Brahmaputra, flows through Upper Siang. The Subansiri, another major tributary of the Brahmaputra, slows through Upper Subansiri.

The increased flooding observed in these rivers may be due to an increase in rainfall. Upper Siang shows a significant decreasing trend in the number of rainy days, which translates to more rainfall over fewer days.

Eight districts in the state showed a significant decreasing rainfall trend. Six other districts showed a decreasing trend as well. Districts like West Kameng and East Kameng in the western part of the state showed a decrease in the flow of mountain springs and streams in recent decades, which could be due to the decrease in rainfall.

These springs and streams are the primary source of water for most villages nestled in the hills and mountains. They later join the tributaries of the Brahmaputra.

The state is mostly hilly with plains limited to a small portion bordering Assam. The state is drained by four major rivers and dozens of minor rivers and tributaries. Dhansiri, Doyang and Dikhu flow northward, down from the Naga hills into the Brahmaputra in Assam; the Tizu flows east towards Myanmar.

Between 1989 and 2018, the state showed a significant decreasing trend in monsoon rainfall. It received less than normal rainfall of 1,143 mm for several years. July received the maximum rainfall in the 30-year period with a variability of almost 30 per cent. The overall monsoon rainfall variation was also high at 24.5 per cent.

Of the 11 districts in the state, seven show a decreasing trend in monsoon rainfall while four districts show an increasing trend. Out of the seven districts where there has been less rainfall five show a significant trend.

In the four districts with increasing trend in rainfall three show a significant trend. Mokokchung district which receives the maximum rainfall during the monsoon period shows a significant decreasing trend. Wokha district which receives the second highest monsoon rainfall among the districts also shows a decreasing trend for the 30-year period.

The gradient of the rivers is not as steep as it is in Arunachal Pradesh, hence the rivers are not as prone to flooding. But recent instance of heavy rainfall events have brought floods to places that had not witnessed the calamity in decades.

Flooding in Wokha district in 2018 is an example. Phek district towards the south of the state showed an overall increase in monsoon rainfall; the number of rainy and heavy rainfall days also increased.

The state is mostly mountainous with high hills, covered with evergreen forests in the south, and snow-capped Himalayan mountains towards the north.

Teesta is the major river flowing through the state. It flows down into West Bengal before entering Bangladesh.

Between 1989 and 2018, the state showed a slight increasing trend in monsoon rainfall with the LPA of 1,609 mm. July received the maximum rainfall with a variability of 23 per cent. The overall monsoon variation was higher than that of Assam, which seems to be the most affected by the changing patterns of the monsoon rainfall in the 30-year period.

The district analysis showed a sharp contrast between the northern and southern parts of the state. Of four districts, the three towards the south — East Sikkim, West Sikkim and South Sikkim — showed a decreasing trend in the monsoon rainfall.

The trend is significant in South and West Sikkim. North Sikkim showed an increasing trend in monsoon rainfall, which may be causing an increase in flooding in the Teesta. This may have led to landslides downstream in South Sikkim, especially around Gangtok, a phenomenon that has been observed since 1997.

In fact, Gangtok became only the second place in the country to get a landslide early warning system in 2018 after Munnar in Kerala.

Only 10 per cent of Manipur is a valley while the rest is hills, divided between the eastern and western hills. Around a fourth of the valley area is covered with lakes, wetlands, barren lands and small hillocks, which leaves a small area for agriculture.

The hills are either not cultivated much or people practice shifting jhum cultivation that is not very productive. The total area under cultivation is 7.4 per cent. Around 67 per cent of the population of the valley is engaged in agriculture, which puts extreme pressure on a small region to feed its entire population.

Therefore, changing rainfall patterns in the state will impact agriculture, in turn affecting the state’s food security.

IMD’s 30 years analysis does not provide data or trends for the state of Manipur. In its overall countrywide statement regarding the analysis, the weather agency mentions the state just once; between 1989 and 2018, the state witnessed a significant increasing trend in heavy rainfall days.

From 1950-2010, July received the maximum average rainfall in the state which was close to 300 mm, according to a 2018 research paper. It also had the maximum variation of around 25 per cent. The paper found a decreasing trend in the intensity of July rainfall.

Another 2016 study by researchers from the Central Agricultural University, Imphal, found that between 1975 and 2007, there were 18 years of mild drought and one year of moderate drought in 1979.

The paper also found that rainfall in the state showed a significant increasing trend from 1975 to 1989 and a significant decreasing trend from 1990 to 2007. Post this, only bits and pieces of information is available.

In 2009, Manipur faced a severe drought. Nearly 46 per cent of the country faced moderate-severe drought then after a highly deficient monsoon. In 2019, Manipur experienced a humongous rain deficit of 56 per cent.

Four districts had large deficient rainfall (deficit greater than 60 per cent below LPA) for the season with a high of 82 per cent deficit in Chandel district.

Four other districts had deficient rainfall (deficit between 20 per cent and 59 per cent below LPA) and one had normal rainfall. The trend continued in 2020, with the state again facing a dry monsoon season with a high deficit of 46 per cent below LPA.

The 2021 season is no better: There was a deficit of 57 per cent (till August 8) in two months. The CAU paper concluded that the decreasing rainfall trends in the state have affected agriculture productivity, especially cultivation of paddy as well as pisciculture.

The state receives 71 per cent of its annual rainfall during the monsoon months. It has mostly hilly terrain with elevations with Khasi hills in centre, Jaintia hills in the east and Garo hills in west and some plain areas of low elevation.

The state has physiographic variation with hills and plants / trees in the northern and western parts; in the south, the slopes are steep and regular with dense forest.

In the 30-year period between 1989 and 2018, the state showed a decreasing trend in the overall rainfall, which got pronounced 2005 onwards.

Yet, East Khasi Hills, the wettest district in the state, saw an increasing rainfall trend, even though it has not been significant. The district has flat-topped hills and several rivers, and has seen significant decrease in the number of rainy days.

Meanwhile, four districts on the east and the west borders saw a significant decrease in rainfall over the period. These districts also recorded a drop in the number of rainy days.

The unpredictability of the region can be guaged from the fact that the rainfall in July, the wettest month in the state, had a variability of 40 per cent.

The state is a land of rolling hills, rivers and valleys. There are 21 major hill ranges of different heights that run through the length and breadth of the state. They are mostly aligned in the south direction in parallel series and are separated by narrow deep rivers and valleys. The hills are extremely rugged and steep. There are some small patches of flat lands.

While the overall rainfall in the 30 years has remained constant, with a minor increase in a month-on-month analysis, the region has seen a substantial drop in the number of rainy days.

This has been accompanied with a substantial increase in heavy rainfall days. The region receives 67 per cent of its annual rainfall during the monsoon months.

Tripura is a landlocked state with small hills and low-lying land with numerous deep rivers and valleys. The hills of the state run from north to south and parallel to one another till they disappear in the plains of Bangladesh.

The hills are mostly of low elevation and are covered by thick forests.

The state has seen a decreasing trend over the 30 years, though it is not significant. The unique challenge, though, is that all districts now receive less overall rains during the monsoon season, which accounts for 60 per cent of its annual rains.

South Tripura, which is the wettest district, with an average annual monsoon rainfall of 1,549 mm, has seen a substantial drop in the number of rainy days and heavy rain events.

All this points to a major climate crisis brewing in the North East India. Watch this space as we follow the story.

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The economic commitment of climate change

Maximilian Kotz ORCID: orcid.org/0000-0003-2564-5043 1 , 2 ,
Anders Levermann ORCID: orcid.org/0000-0003-4432-4704 1 , 2 &
Leonie Wenz ORCID: orcid.org/0000-0002-8500-1568 1 , 3

Nature volume 628 , pages 551–557 ( 2024 ) Cite this article

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Environmental economics
Environmental health
Interdisciplinary studies
Projection and prediction

Global projections of macroeconomic climate-change damages typically consider impacts from average annual and national temperatures over long time horizons 1 , 2 , 3 , 4 , 5 , 6 . Here we use recent empirical findings from more than 1,600 regions worldwide over the past 40 years to project sub-national damages from temperature and precipitation, including daily variability and extremes 7 , 8 . Using an empirical approach that provides a robust lower bound on the persistence of impacts on economic growth, we find that the world economy is committed to an income reduction of 19% within the next 26 years independent of future emission choices (relative to a baseline without climate impacts, likely range of 11–29% accounting for physical climate and empirical uncertainty). These damages already outweigh the mitigation costs required to limit global warming to 2 °C by sixfold over this near-term time frame and thereafter diverge strongly dependent on emission choices. Committed damages arise predominantly through changes in average temperature, but accounting for further climatic components raises estimates by approximately 50% and leads to stronger regional heterogeneity. Committed losses are projected for all regions except those at very high latitudes, at which reductions in temperature variability bring benefits. The largest losses are committed at lower latitudes in regions with lower cumulative historical emissions and lower present-day income.

Climate damage projections beyond annual temperature

Investment incentive reduced by climate damages can be restored by optimal policy

Climate economics support for the UN climate targets

Projections of the macroeconomic damage caused by future climate change are crucial to informing public and policy debates about adaptation, mitigation and climate justice. On the one hand, adaptation against climate impacts must be justified and planned on the basis of an understanding of their future magnitude and spatial distribution 9 . This is also of importance in the context of climate justice 10 , as well as to key societal actors, including governments, central banks and private businesses, which increasingly require the inclusion of climate risks in their macroeconomic forecasts to aid adaptive decision-making 11 , 12 . On the other hand, climate mitigation policy such as the Paris Climate Agreement is often evaluated by balancing the costs of its implementation against the benefits of avoiding projected physical damages. This evaluation occurs both formally through cost–benefit analyses 1 , 4 , 5 , 6 , as well as informally through public perception of mitigation and damage costs 13 .

Projections of future damages meet challenges when informing these debates, in particular the human biases relating to uncertainty and remoteness that are raised by long-term perspectives 14 . Here we aim to overcome such challenges by assessing the extent of economic damages from climate change to which the world is already committed by historical emissions and socio-economic inertia (the range of future emission scenarios that are considered socio-economically plausible 15 ). Such a focus on the near term limits the large uncertainties about diverging future emission trajectories, the resulting long-term climate response and the validity of applying historically observed climate–economic relations over long timescales during which socio-technical conditions may change considerably. As such, this focus aims to simplify the communication and maximize the credibility of projected economic damages from future climate change.

In projecting the future economic damages from climate change, we make use of recent advances in climate econometrics that provide evidence for impacts on sub-national economic growth from numerous components of the distribution of daily temperature and precipitation 3 , 7 , 8 . Using fixed-effects panel regression models to control for potential confounders, these studies exploit within-region variation in local temperature and precipitation in a panel of more than 1,600 regions worldwide, comprising climate and income data over the past 40 years, to identify the plausibly causal effects of changes in several climate variables on economic productivity 16 , 17 . Specifically, macroeconomic impacts have been identified from changing daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall that occur in addition to those already identified from changing average temperature 2 , 3 , 18 . Moreover, regional heterogeneity in these effects based on the prevailing local climatic conditions has been found using interactions terms. The selection of these climate variables follows micro-level evidence for mechanisms related to the impacts of average temperatures on labour and agricultural productivity 2 , of temperature variability on agricultural productivity and health 7 , as well as of precipitation on agricultural productivity, labour outcomes and flood damages 8 (see Extended Data Table 1 for an overview, including more detailed references). References 7 , 8 contain a more detailed motivation for the use of these particular climate variables and provide extensive empirical tests about the robustness and nature of their effects on economic output, which are summarized in Methods . By accounting for these extra climatic variables at the sub-national level, we aim for a more comprehensive description of climate impacts with greater detail across both time and space.

Constraining the persistence of impacts

A key determinant and source of discrepancy in estimates of the magnitude of future climate damages is the extent to which the impact of a climate variable on economic growth rates persists. The two extreme cases in which these impacts persist indefinitely or only instantaneously are commonly referred to as growth or level effects 19 , 20 (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for mathematical definitions). Recent work shows that future damages from climate change depend strongly on whether growth or level effects are assumed 20 . Following refs. 2 , 18 , we provide constraints on this persistence by using distributed lag models to test the significance of delayed effects separately for each climate variable. Notably, and in contrast to refs. 2 , 18 , we use climate variables in their first-differenced form following ref. 3 , implying a dependence of the growth rate on a change in climate variables. This choice means that a baseline specification without any lags constitutes a model prior of purely level effects, in which a permanent change in the climate has only an instantaneous effect on the growth rate 3 , 19 , 21 . By including lags, one can then test whether any effects may persist further. This is in contrast to the specification used by refs. 2 , 18 , in which climate variables are used without taking the first difference, implying a dependence of the growth rate on the level of climate variables. In this alternative case, the baseline specification without any lags constitutes a model prior of pure growth effects, in which a change in climate has an infinitely persistent effect on the growth rate. Consequently, including further lags in this alternative case tests whether the initial growth impact is recovered 18 , 19 , 21 . Both of these specifications suffer from the limiting possibility that, if too few lags are included, one might falsely accept the model prior. The limitations of including a very large number of lags, including loss of data and increasing statistical uncertainty with an increasing number of parameters, mean that such a possibility is likely. By choosing a specification in which the model prior is one of level effects, our approach is therefore conservative by design, avoiding assumptions of infinite persistence of climate impacts on growth and instead providing a lower bound on this persistence based on what is observable empirically (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for further exposition of this framework). The conservative nature of such a choice is probably the reason that ref. 19 finds much greater consistency between the impacts projected by models that use the first difference of climate variables, as opposed to their levels.

We begin our empirical analysis of the persistence of climate impacts on growth using ten lags of the first-differenced climate variables in fixed-effects distributed lag models. We detect substantial effects on economic growth at time lags of up to approximately 8–10 years for the temperature terms and up to approximately 4 years for the precipitation terms (Extended Data Fig. 1 and Extended Data Table 2 ). Furthermore, evaluation by means of information criteria indicates that the inclusion of all five climate variables and the use of these numbers of lags provide a preferable trade-off between best-fitting the data and including further terms that could cause overfitting, in comparison with model specifications excluding climate variables or including more or fewer lags (Extended Data Fig. 3 , Supplementary Methods Section 1 and Supplementary Table 1 ). We therefore remove statistically insignificant terms at later lags (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). Further tests using Monte Carlo simulations demonstrate that the empirical models are robust to autocorrelation in the lagged climate variables (Supplementary Methods Section 2 and Supplementary Figs. 4 and 5 ), that information criteria provide an effective indicator for lag selection (Supplementary Methods Section 2 and Supplementary Fig. 6 ), that the results are robust to concerns of imperfect multicollinearity between climate variables and that including several climate variables is actually necessary to isolate their separate effects (Supplementary Methods Section 3 and Supplementary Fig. 7 ). We provide a further robustness check using a restricted distributed lag model to limit oscillations in the lagged parameter estimates that may result from autocorrelation, finding that it provides similar estimates of cumulative marginal effects to the unrestricted model (Supplementary Methods Section 4 and Supplementary Figs. 8 and 9 ). Finally, to explicitly account for any outstanding uncertainty arising from the precise choice of the number of lags, we include empirical models with marginally different numbers of lags in the error-sampling procedure of our projection of future damages. On the basis of the lag-selection procedure (the significance of lagged terms in Extended Data Fig. 1 and Extended Data Table 2 , as well as information criteria in Extended Data Fig. 3 ), we sample from models with eight to ten lags for temperature and four for precipitation (models shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). In summary, this empirical approach to constrain the persistence of climate impacts on economic growth rates is conservative by design in avoiding assumptions of infinite persistence, but nevertheless provides a lower bound on the extent of impact persistence that is robust to the numerous tests outlined above.

Committed damages until mid-century

We combine these empirical economic response functions (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) with an ensemble of 21 climate models (see Supplementary Table 5 ) from the Coupled Model Intercomparison Project Phase 6 (CMIP-6) 22 to project the macroeconomic damages from these components of physical climate change (see Methods for further details). Bias-adjusted climate models that provide a highly accurate reproduction of observed climatological patterns with limited uncertainty (Supplementary Table 6 ) are used to avoid introducing biases in the projections. Following a well-developed literature 2 , 3 , 19 , these projections do not aim to provide a prediction of future economic growth. Instead, they are a projection of the exogenous impact of future climate conditions on the economy relative to the baselines specified by socio-economic projections, based on the plausibly causal relationships inferred by the empirical models and assuming ceteris paribus. Other exogenous factors relevant for the prediction of economic output are purposefully assumed constant.

A Monte Carlo procedure that samples from climate model projections, empirical models with different numbers of lags and model parameter estimates (obtained by 1,000 block-bootstrap resamples of each of the regressions in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) is used to estimate the combined uncertainty from these sources. Given these uncertainty distributions, we find that projected global damages are statistically indistinguishable across the two most extreme emission scenarios until 2049 (at the 5% significance level; Fig. 1 ). As such, the climate damages occurring before this time constitute those to which the world is already committed owing to the combination of past emissions and the range of future emission scenarios that are considered socio-economically plausible 15 . These committed damages comprise a permanent income reduction of 19% on average globally (population-weighted average) in comparison with a baseline without climate-change impacts (with a likely range of 11–29%, following the likelihood classification adopted by the Intergovernmental Panel on Climate Change (IPCC); see caption of Fig. 1 ). Even though levels of income per capita generally still increase relative to those of today, this constitutes a permanent income reduction for most regions, including North America and Europe (each with median income reductions of approximately 11%) and with South Asia and Africa being the most strongly affected (each with median income reductions of approximately 22%; Fig. 1 ). Under a middle-of-the road scenario of future income development (SSP2, in which SSP stands for Shared Socio-economic Pathway), this corresponds to global annual damages in 2049 of 38 trillion in 2005 international dollars (likely range of 19–59 trillion 2005 international dollars). Compared with empirical specifications that assume pure growth or pure level effects, our preferred specification that provides a robust lower bound on the extent of climate impact persistence produces damages between these two extreme assumptions (Extended Data Fig. 3 ).

Estimates of the projected reduction in income per capita from changes in all climate variables based on empirical models of climate impacts on economic output with a robust lower bound on their persistence (Extended Data Fig. 1 ) under a low-emission scenario compatible with the 2 °C warming target and a high-emission scenario (SSP2-RCP2.6 and SSP5-RCP8.5, respectively) are shown in purple and orange, respectively. Shading represents the 34% and 10% confidence intervals reflecting the likely and very likely ranges, respectively (following the likelihood classification adopted by the IPCC), having estimated uncertainty from a Monte Carlo procedure, which samples the uncertainty from the choice of physical climate models, empirical models with different numbers of lags and bootstrapped estimates of the regression parameters shown in Supplementary Figs. 1 – 3 . Vertical dashed lines show the time at which the climate damages of the two emission scenarios diverge at the 5% and 1% significance levels based on the distribution of differences between emission scenarios arising from the uncertainty sampling discussed above. Note that uncertainty in the difference of the two scenarios is smaller than the combined uncertainty of the two respective scenarios because samples of the uncertainty (climate model and empirical model choice, as well as model parameter bootstrap) are consistent across the two emission scenarios, hence the divergence of damages occurs while the uncertainty bounds of the two separate damage scenarios still overlap. Estimates of global mitigation costs from the three IAMs that provide results for the SSP2 baseline and SSP2-RCP2.6 scenario are shown in light green in the top panel, with the median of these estimates shown in bold.

Damages already outweigh mitigation costs

We compare the damages to which the world is committed over the next 25 years to estimates of the mitigation costs required to achieve the Paris Climate Agreement. Taking estimates of mitigation costs from the three integrated assessment models (IAMs) in the IPCC AR6 database 23 that provide results under comparable scenarios (SSP2 baseline and SSP2-RCP2.6, in which RCP stands for Representative Concentration Pathway), we find that the median committed climate damages are larger than the median mitigation costs in 2050 (six trillion in 2005 international dollars) by a factor of approximately six (note that estimates of mitigation costs are only provided every 10 years by the IAMs and so a comparison in 2049 is not possible). This comparison simply aims to compare the magnitude of future damages against mitigation costs, rather than to conduct a formal cost–benefit analysis of transitioning from one emission path to another. Formal cost–benefit analyses typically find that the net benefits of mitigation only emerge after 2050 (ref. 5 ), which may lead some to conclude that physical damages from climate change are simply not large enough to outweigh mitigation costs until the second half of the century. Our simple comparison of their magnitudes makes clear that damages are actually already considerably larger than mitigation costs and the delayed emergence of net mitigation benefits results primarily from the fact that damages across different emission paths are indistinguishable until mid-century (Fig. 1 ).

Although these near-term damages constitute those to which the world is already committed, we note that damage estimates diverge strongly across emission scenarios after 2049, conveying the clear benefits of mitigation from a purely economic point of view that have been emphasized in previous studies 4 , 24 . As well as the uncertainties assessed in Fig. 1 , these conclusions are robust to structural choices, such as the timescale with which changes in the moderating variables of the empirical models are estimated (Supplementary Figs. 10 and 11 ), as well as the order in which one accounts for the intertemporal and international components of currency comparison (Supplementary Fig. 12 ; see Methods for further details).

Damages from variability and extremes

Committed damages primarily arise through changes in average temperature (Fig. 2 ). This reflects the fact that projected changes in average temperature are larger than those in other climate variables when expressed as a function of their historical interannual variability (Extended Data Fig. 4 ). Because the historical variability is that on which the empirical models are estimated, larger projected changes in comparison with this variability probably lead to larger future impacts in a purely statistical sense. From a mechanistic perspective, one may plausibly interpret this result as implying that future changes in average temperature are the most unprecedented from the perspective of the historical fluctuations to which the economy is accustomed and therefore will cause the most damage. This insight may prove useful in terms of guiding adaptation measures to the sources of greatest damage.

Estimates of the median projected reduction in sub-national income per capita across emission scenarios (SSP2-RCP2.6 and SSP2-RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ). a , Impacts arising from all climate variables. b – f , Impacts arising separately from changes in annual mean temperature ( b ), daily temperature variability ( c ), total annual precipitation ( d ), the annual number of wet days (>1 mm) ( e ) and extreme daily rainfall ( f ) (see Methods for further definitions). Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Nevertheless, future damages based on empirical models that consider changes in annual average temperature only and exclude the other climate variables constitute income reductions of only 13% in 2049 (Extended Data Fig. 5a , likely range 5–21%). This suggests that accounting for the other components of the distribution of temperature and precipitation raises net damages by nearly 50%. This increase arises through the further damages that these climatic components cause, but also because their inclusion reveals a stronger negative economic response to average temperatures (Extended Data Fig. 5b ). The latter finding is consistent with our Monte Carlo simulations, which suggest that the magnitude of the effect of average temperature on economic growth is underestimated unless accounting for the impacts of other correlated climate variables (Supplementary Fig. 7 ).

In terms of the relative contributions of the different climatic components to overall damages, we find that accounting for daily temperature variability causes the largest increase in overall damages relative to empirical frameworks that only consider changes in annual average temperature (4.9 percentage points, likely range 2.4–8.7 percentage points, equivalent to approximately 10 trillion international dollars). Accounting for precipitation causes smaller increases in overall damages, which are—nevertheless—equivalent to approximately 1.2 trillion international dollars: 0.01 percentage points (−0.37–0.33 percentage points), 0.34 percentage points (0.07–0.90 percentage points) and 0.36 percentage points (0.13–0.65 percentage points) from total annual precipitation, the number of wet days and extreme daily precipitation, respectively. Moreover, climate models seem to underestimate future changes in temperature variability 25 and extreme precipitation 26 , 27 in response to anthropogenic forcing as compared with that observed historically, suggesting that the true impacts from these variables may be larger.

The distribution of committed damages

The spatial distribution of committed damages (Fig. 2a ) reflects a complex interplay between the patterns of future change in several climatic components and those of historical economic vulnerability to changes in those variables. Damages resulting from increasing annual mean temperature (Fig. 2b ) are negative almost everywhere globally, and larger at lower latitudes in regions in which temperatures are already higher and economic vulnerability to temperature increases is greatest (see the response heterogeneity to mean temperature embodied in Extended Data Fig. 1a ). This occurs despite the amplified warming projected at higher latitudes 28 , suggesting that regional heterogeneity in economic vulnerability to temperature changes outweighs heterogeneity in the magnitude of future warming (Supplementary Fig. 13a ). Economic damages owing to daily temperature variability (Fig. 2c ) exhibit a strong latitudinal polarisation, primarily reflecting the physical response of daily variability to greenhouse forcing in which increases in variability across lower latitudes (and Europe) contrast decreases at high latitudes 25 (Supplementary Fig. 13b ). These two temperature terms are the dominant determinants of the pattern of overall damages (Fig. 2a ), which exhibits a strong polarity with damages across most of the globe except at the highest northern latitudes. Future changes in total annual precipitation mainly bring economic benefits except in regions of drying, such as the Mediterranean and central South America (Fig. 2d and Supplementary Fig. 13c ), but these benefits are opposed by changes in the number of wet days, which produce damages with a similar pattern of opposite sign (Fig. 2e and Supplementary Fig. 13d ). By contrast, changes in extreme daily rainfall produce damages in all regions, reflecting the intensification of daily rainfall extremes over global land areas 29 , 30 (Fig. 2f and Supplementary Fig. 13e ).

The spatial distribution of committed damages implies considerable injustice along two dimensions: culpability for the historical emissions that have caused climate change and pre-existing levels of socio-economic welfare. Spearman’s rank correlations indicate that committed damages are significantly larger in countries with smaller historical cumulative emissions, as well as in regions with lower current income per capita (Fig. 3 ). This implies that those countries that will suffer the most from the damages already committed are those that are least responsible for climate change and which also have the least resources to adapt to it.

Estimates of the median projected change in national income per capita across emission scenarios (RCP2.6 and RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ) are plotted against cumulative national emissions per capita in 2020 (from the Global Carbon Project) and coloured by national income per capita in 2020 (from the World Bank) in a and vice versa in b . In each panel, the size of each scatter point is weighted by the national population in 2020 (from the World Bank). Inset numbers indicate the Spearman’s rank correlation ρ and P -values for a hypothesis test whose null hypothesis is of no correlation, as well as the Spearman’s rank correlation weighted by national population.

To further quantify this heterogeneity, we assess the difference in committed damages between the upper and lower quartiles of regions when ranked by present income levels and historical cumulative emissions (using a population weighting to both define the quartiles and estimate the group averages). On average, the quartile of countries with lower income are committed to an income loss that is 8.9 percentage points (or 61%) greater than the upper quartile (Extended Data Fig. 6 ), with a likely range of 3.8–14.7 percentage points across the uncertainty sampling of our damage projections (following the likelihood classification adopted by the IPCC). Similarly, the quartile of countries with lower historical cumulative emissions are committed to an income loss that is 6.9 percentage points (or 40%) greater than the upper quartile, with a likely range of 0.27–12 percentage points. These patterns reemphasize the prevalence of injustice in climate impacts 31 , 32 , 33 in the context of the damages to which the world is already committed by historical emissions and socio-economic inertia.

Contextualizing the magnitude of damages

The magnitude of projected economic damages exceeds previous literature estimates 2 , 3 , arising from several developments made on previous approaches. Our estimates are larger than those of ref. 2 (see first row of Extended Data Table 3 ), primarily because of the facts that sub-national estimates typically show a steeper temperature response (see also refs. 3 , 34 ) and that accounting for other climatic components raises damage estimates (Extended Data Fig. 5 ). However, we note that our empirical approach using first-differenced climate variables is conservative compared with that of ref. 2 in regard to the persistence of climate impacts on growth (see introduction and Methods section ‘Empirical model specification: fixed-effects distributed lag models’), an important determinant of the magnitude of long-term damages 19 , 21 . Using a similar empirical specification to ref. 2 , which assumes infinite persistence while maintaining the rest of our approach (sub-national data and further climate variables), produces considerably larger damages (purple curve of Extended Data Fig. 3 ). Compared with studies that do take the first difference of climate variables 3 , 35 , our estimates are also larger (see second and third rows of Extended Data Table 3 ). The inclusion of further climate variables (Extended Data Fig. 5 ) and a sufficient number of lags to more adequately capture the extent of impact persistence (Extended Data Figs. 1 and 2 ) are the main sources of this difference, as is the use of specifications that capture nonlinearities in the temperature response when compared with ref. 35 . In summary, our estimates develop on previous studies by incorporating the latest data and empirical insights 7 , 8 , as well as in providing a robust empirical lower bound on the persistence of impacts on economic growth, which constitutes a middle ground between the extremes of the growth-versus-levels debate 19 , 21 (Extended Data Fig. 3 ).

Compared with the fraction of variance explained by the empirical models historically (<5%), the projection of reductions in income of 19% may seem large. This arises owing to the fact that projected changes in climatic conditions are much larger than those that were experienced historically, particularly for changes in average temperature (Extended Data Fig. 4 ). As such, any assessment of future climate-change impacts necessarily requires an extrapolation outside the range of the historical data on which the empirical impact models were evaluated. Nevertheless, these models constitute the most state-of-the-art methods for inference of plausibly causal climate impacts based on observed data. Moreover, we take explicit steps to limit out-of-sample extrapolation by capping the moderating variables of the interaction terms at the 95th percentile of the historical distribution (see Methods ). This avoids extrapolating the marginal effects outside what was observed historically. Given the nonlinear response of economic output to annual mean temperature (Extended Data Fig. 1 and Extended Data Table 2 ), this is a conservative choice that limits the magnitude of damages that we project. Furthermore, back-of-the-envelope calculations indicate that the projected damages are consistent with the magnitude and patterns of historical economic development (see Supplementary Discussion Section 5 ).

Missing impacts and spatial spillovers

Despite assessing several climatic components from which economic impacts have recently been identified 3 , 7 , 8 , this assessment of aggregate climate damages should not be considered comprehensive. Important channels such as impacts from heatwaves 31 , sea-level rise 36 , tropical cyclones 37 and tipping points 38 , 39 , as well as non-market damages such as those to ecosystems 40 and human health 41 , are not considered in these estimates. Sea-level rise is unlikely to be feasibly incorporated into empirical assessments such as this because historical sea-level variability is mostly small. Non-market damages are inherently intractable within our estimates of impacts on aggregate monetary output and estimates of these impacts could arguably be considered as extra to those identified here. Recent empirical work suggests that accounting for these channels would probably raise estimates of these committed damages, with larger damages continuing to arise in the global south 31 , 36 , 37 , 38 , 39 , 40 , 41 , 42 .

Moreover, our main empirical analysis does not explicitly evaluate the potential for impacts in local regions to produce effects that ‘spill over’ into other regions. Such effects may further mitigate or amplify the impacts we estimate, for example, if companies relocate production from one affected region to another or if impacts propagate along supply chains. The current literature indicates that trade plays a substantial role in propagating spillover effects 43 , 44 , making their assessment at the sub-national level challenging without available data on sub-national trade dependencies. Studies accounting for only spatially adjacent neighbours indicate that negative impacts in one region induce further negative impacts in neighbouring regions 45 , 46 , 47 , 48 , suggesting that our projected damages are probably conservative by excluding these effects. In Supplementary Fig. 14 , we assess spillovers from neighbouring regions using a spatial-lag model. For simplicity, this analysis excludes temporal lags, focusing only on contemporaneous effects. The results show that accounting for spatial spillovers can amplify the overall magnitude, and also the heterogeneity, of impacts. Consistent with previous literature, this indicates that the overall magnitude (Fig. 1 ) and heterogeneity (Fig. 3 ) of damages that we project in our main specification may be conservative without explicitly accounting for spillovers. We note that further analysis that addresses both spatially and trade-connected spillovers, while also accounting for delayed impacts using temporal lags, would be necessary to adequately address this question fully. These approaches offer fruitful avenues for further research but are beyond the scope of this manuscript, which primarily aims to explore the impacts of different climate conditions and their persistence.

Policy implications

We find that the economic damages resulting from climate change until 2049 are those to which the world economy is already committed and that these greatly outweigh the costs required to mitigate emissions in line with the 2 °C target of the Paris Climate Agreement (Fig. 1 ). This assessment is complementary to formal analyses of the net costs and benefits associated with moving from one emission path to another, which typically find that net benefits of mitigation only emerge in the second half of the century 5 . Our simple comparison of the magnitude of damages and mitigation costs makes clear that this is primarily because damages are indistinguishable across emissions scenarios—that is, committed—until mid-century (Fig. 1 ) and that they are actually already much larger than mitigation costs. For simplicity, and owing to the availability of data, we compare damages to mitigation costs at the global level. Regional estimates of mitigation costs may shed further light on the national incentives for mitigation to which our results already hint, of relevance for international climate policy. Although these damages are committed from a mitigation perspective, adaptation may provide an opportunity to reduce them. Moreover, the strong divergence of damages after mid-century reemphasizes the clear benefits of mitigation from a purely economic perspective, as highlighted in previous studies 1 , 4 , 6 , 24 .

Historical climate data

Historical daily 2-m temperature and precipitation totals (in mm) are obtained for the period 1979–2019 from the W5E5 database. The W5E5 dataset comes from ERA-5, a state-of-the-art reanalysis of historical observations, but has been bias-adjusted by applying version 2.0 of the WATCH Forcing Data to ERA-5 reanalysis data and precipitation data from version 2.3 of the Global Precipitation Climatology Project to better reflect ground-based measurements 49 , 50 , 51 . We obtain these data on a 0.5° × 0.5° grid from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) database. Notably, these historical data have been used to bias-adjust future climate projections from CMIP-6 (see the following section), ensuring consistency between the distribution of historical daily weather on which our empirical models were estimated and the climate projections used to estimate future damages. These data are publicly available from the ISIMIP database. See refs. 7 , 8 for robustness tests of the empirical models to the choice of climate data reanalysis products.

Future climate data

Daily 2-m temperature and precipitation totals (in mm) are taken from 21 climate models participating in CMIP-6 under a high (RCP8.5) and a low (RCP2.6) greenhouse gas emission scenario from 2015 to 2100. The data have been bias-adjusted and statistically downscaled to a common half-degree grid to reflect the historical distribution of daily temperature and precipitation of the W5E5 dataset using the trend-preserving method developed by the ISIMIP 50 , 52 . As such, the climate model data reproduce observed climatological patterns exceptionally well (Supplementary Table 5 ). Gridded data are publicly available from the ISIMIP database.

Historical economic data

Historical economic data come from the DOSE database of sub-national economic output 53 . We use a recent revision to the DOSE dataset that provides data across 83 countries, 1,660 sub-national regions with varying temporal coverage from 1960 to 2019. Sub-national units constitute the first administrative division below national, for example, states for the USA and provinces for China. Data come from measures of gross regional product per capita (GRPpc) or income per capita in local currencies, reflecting the values reported in national statistical agencies, yearbooks and, in some cases, academic literature. We follow previous literature 3 , 7 , 8 , 54 and assess real sub-national output per capita by first converting values from local currencies to US dollars to account for diverging national inflationary tendencies and then account for US inflation using a US deflator. Alternatively, one might first account for national inflation and then convert between currencies. Supplementary Fig. 12 demonstrates that our conclusions are consistent when accounting for price changes in the reversed order, although the magnitude of estimated damages varies. See the documentation of the DOSE dataset for further discussion of these choices. Conversions between currencies are conducted using exchange rates from the FRED database of the Federal Reserve Bank of St. Louis 55 and the national deflators from the World Bank 56 .

Future socio-economic data

Baseline gridded gross domestic product (GDP) and population data for the period 2015–2100 are taken from the middle-of-the-road scenario SSP2 (ref. 15 ). Population data have been downscaled to a half-degree grid by the ISIMIP following the methodologies of refs. 57 , 58 , which we then aggregate to the sub-national level of our economic data using the spatial aggregation procedure described below. Because current methodologies for downscaling the GDP of the SSPs use downscaled population to do so, per-capita estimates of GDP with a realistic distribution at the sub-national level are not readily available for the SSPs. We therefore use national-level GDP per capita (GDPpc) projections for all sub-national regions of a given country, assuming homogeneity within countries in terms of baseline GDPpc. Here we use projections that have been updated to account for the impact of the COVID-19 pandemic on the trajectory of future income, while remaining consistent with the long-term development of the SSPs 59 . The choice of baseline SSP alters the magnitude of projected climate damages in monetary terms, but when assessed in terms of percentage change from the baseline, the choice of socio-economic scenario is inconsequential. Gridded SSP population data and national-level GDPpc data are publicly available from the ISIMIP database. Sub-national estimates as used in this study are available in the code and data replication files.

Climate variables

Following recent literature 3 , 7 , 8 , we calculate an array of climate variables for which substantial impacts on macroeconomic output have been identified empirically, supported by further evidence at the micro level for plausible underlying mechanisms. See refs. 7 , 8 for an extensive motivation for the use of these particular climate variables and for detailed empirical tests on the nature and robustness of their effects on economic output. To summarize, these studies have found evidence for independent impacts on economic growth rates from annual average temperature, daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall. Assessments of daily temperature variability were motivated by evidence of impacts on agricultural output and human health, as well as macroeconomic literature on the impacts of volatility on growth when manifest in different dimensions, such as government spending, exchange rates and even output itself 7 . Assessments of precipitation impacts were motivated by evidence of impacts on agricultural productivity, metropolitan labour outcomes and conflict, as well as damages caused by flash flooding 8 . See Extended Data Table 1 for detailed references to empirical studies of these physical mechanisms. Marked impacts of daily temperature variability, total annual precipitation, the number of wet days and extreme daily rainfall on macroeconomic output were identified robustly across different climate datasets, spatial aggregation schemes, specifications of regional time trends and error-clustering approaches. They were also found to be robust to the consideration of temperature extremes 7 , 8 . Furthermore, these climate variables were identified as having independent effects on economic output 7 , 8 , which we further explain here using Monte Carlo simulations to demonstrate the robustness of the results to concerns of imperfect multicollinearity between climate variables (Supplementary Methods Section 2 ), as well as by using information criteria (Supplementary Table 1 ) to demonstrate that including several lagged climate variables provides a preferable trade-off between optimally describing the data and limiting the possibility of overfitting.

We calculate these variables from the distribution of daily, d , temperature, T x , d , and precipitation, P x , d , at the grid-cell, x , level for both the historical and future climate data. As well as annual mean temperature, ${\bar{T}}_{x,y}$ , and annual total precipitation, P x , y , we calculate annual, y , measures of daily temperature variability, ${\widetilde{T}}_{x,y}$ :

the number of wet days, Pwd x , y :

and extreme daily rainfall:

in which T x , d , m , y is the grid-cell-specific daily temperature in month m and year y , ${\bar{T}}_{x,m,{y}}$ is the year and grid-cell-specific monthly, m , mean temperature, D m and D y the number of days in a given month m or year y , respectively, H the Heaviside step function, 1 mm the threshold used to define wet days and P 99.9 x is the 99.9th percentile of historical (1979–2019) daily precipitation at the grid-cell level. Units of the climate measures are degrees Celsius for annual mean temperature and daily temperature variability, millimetres for total annual precipitation and extreme daily precipitation, and simply the number of days for the annual number of wet days.

We also calculated weighted standard deviations of monthly rainfall totals as also used in ref. 8 but do not include them in our projections as we find that, when accounting for delayed effects, their effect becomes statistically indistinct and is better captured by changes in total annual rainfall.

Spatial aggregation

We aggregate grid-cell-level historical and future climate measures, as well as grid-cell-level future GDPpc and population, to the level of the first administrative unit below national level of the GADM database, using an area-weighting algorithm that estimates the portion of each grid cell falling within an administrative boundary. We use this as our baseline specification following previous findings that the effect of area or population weighting at the sub-national level is negligible 7 , 8 .

Empirical model specification: fixed-effects distributed lag models

Following a wide range of climate econometric literature 16 , 60 , we use panel regression models with a selection of fixed effects and time trends to isolate plausibly exogenous variation with which to maximize confidence in a causal interpretation of the effects of climate on economic growth rates. The use of region fixed effects, μ r , accounts for unobserved time-invariant differences between regions, such as prevailing climatic norms and growth rates owing to historical and geopolitical factors. The use of yearly fixed effects, η y , accounts for regionally invariant annual shocks to the global climate or economy such as the El Niño–Southern Oscillation or global recessions. In our baseline specification, we also include region-specific linear time trends, k r y , to exclude the possibility of spurious correlations resulting from common slow-moving trends in climate and growth.

The persistence of climate impacts on economic growth rates is a key determinant of the long-term magnitude of damages. Methods for inferring the extent of persistence in impacts on growth rates have typically used lagged climate variables to evaluate the presence of delayed effects or catch-up dynamics 2 , 18 . For example, consider starting from a model in which a climate condition, C r , y , (for example, annual mean temperature) affects the growth rate, Δlgrp r , y (the first difference of the logarithm of gross regional product) of region r in year y :

which we refer to as a ‘pure growth effects’ model in the main text. Typically, further lags are included,

and the cumulative effect of all lagged terms is evaluated to assess the extent to which climate impacts on growth rates persist. Following ref. 18 , in the case that,

the implication is that impacts on the growth rate persist up to NL years after the initial shock (possibly to a weaker or a stronger extent), whereas if

then the initial impact on the growth rate is recovered after NL years and the effect is only one on the level of output. However, we note that such approaches are limited by the fact that, when including an insufficient number of lags to detect a recovery of the growth rates, one may find equation ( 6 ) to be satisfied and incorrectly assume that a change in climatic conditions affects the growth rate indefinitely. In practice, given a limited record of historical data, including too few lags to confidently conclude in an infinitely persistent impact on the growth rate is likely, particularly over the long timescales over which future climate damages are often projected 2 , 24 . To avoid this issue, we instead begin our analysis with a model for which the level of output, lgrp r , y , depends on the level of a climate variable, C r , y :

Given the non-stationarity of the level of output, we follow the literature 19 and estimate such an equation in first-differenced form as,

which we refer to as a model of ‘pure level effects’ in the main text. This model constitutes a baseline specification in which a permanent change in the climate variable produces an instantaneous impact on the growth rate and a permanent effect only on the level of output. By including lagged variables in this specification,

we are able to test whether the impacts on the growth rate persist any further than instantaneously by evaluating whether α L > 0 are statistically significantly different from zero. Even though this framework is also limited by the possibility of including too few lags, the choice of a baseline model specification in which impacts on the growth rate do not persist means that, in the case of including too few lags, the framework reverts to the baseline specification of level effects. As such, this framework is conservative with respect to the persistence of impacts and the magnitude of future damages. It naturally avoids assumptions of infinite persistence and we are able to interpret any persistence that we identify with equation ( 9 ) as a lower bound on the extent of climate impact persistence on growth rates. See the main text for further discussion of this specification choice, in particular about its conservative nature compared with previous literature estimates, such as refs. 2 , 18 .

We allow the response to climatic changes to vary across regions, using interactions of the climate variables with historical average (1979–2019) climatic conditions reflecting heterogenous effects identified in previous work 7 , 8 . Following this previous work, the moderating variables of these interaction terms constitute the historical average of either the variable itself or of the seasonal temperature difference, ${\hat{T}}_{r}$ , or annual mean temperature, ${\bar{T}}_{r}$ , in the case of daily temperature variability 7 and extreme daily rainfall, respectively 8 .

The resulting regression equation with N and M lagged variables, respectively, reads:

in which Δlgrp r , y is the annual, regional GRPpc growth rate, measured as the first difference of the logarithm of real GRPpc, following previous work 2 , 3 , 7 , 8 , 18 , 19 . Fixed-effects regressions were run using the fixest package in R (ref. 61 ).

Estimates of the coefficients of interest α i , L are shown in Extended Data Fig. 1 for N = M = 10 lags and for our preferred choice of the number of lags in Supplementary Figs. 1 – 3 . In Extended Data Fig. 1 , errors are shown clustered at the regional level, but for the construction of damage projections, we block-bootstrap the regressions by region 1,000 times to provide a range of parameter estimates with which to sample the projection uncertainty (following refs. 2 , 31 ).

Spatial-lag model

In Supplementary Fig. 14 , we present the results from a spatial-lag model that explores the potential for climate impacts to ‘spill over’ into spatially neighbouring regions. We measure the distance between centroids of each pair of sub-national regions and construct spatial lags that take the average of the first-differenced climate variables and their interaction terms over neighbouring regions that are at distances of 0–500, 500–1,000, 1,000–1,500 and 1,500–2000 km (spatial lags, ‘SL’, 1 to 4). For simplicity, we then assess a spatial-lag model without temporal lags to assess spatial spillovers of contemporaneous climate impacts. This model takes the form:

in which SL indicates the spatial lag of each climate variable and interaction term. In Supplementary Fig. 14 , we plot the cumulative marginal effect of each climate variable at different baseline climate conditions by summing the coefficients for each climate variable and interaction term, for example, for average temperature impacts as:

These cumulative marginal effects can be regarded as the overall spatially dependent impact to an individual region given a one-unit shock to a climate variable in that region and all neighbouring regions at a given value of the moderating variable of the interaction term.

Constructing projections of economic damage from future climate change

We construct projections of future climate damages by applying the coefficients estimated in equation ( 10 ) and shown in Supplementary Tables 2 – 4 (when including only lags with statistically significant effects in specifications that limit overfitting; see Supplementary Methods Section 1 ) to projections of future climate change from the CMIP-6 models. Year-on-year changes in each primary climate variable of interest are calculated to reflect the year-to-year variations used in the empirical models. 30-year moving averages of the moderating variables of the interaction terms are calculated to reflect the long-term average of climatic conditions that were used for the moderating variables in the empirical models. By using moving averages in the projections, we account for the changing vulnerability to climate shocks based on the evolving long-term conditions (Supplementary Figs. 10 and 11 show that the results are robust to the precise choice of the window of this moving average). Although these climate variables are not differenced, the fact that the bias-adjusted climate models reproduce observed climatological patterns across regions for these moderating variables very accurately (Supplementary Table 6 ) with limited spread across models (<3%) precludes the possibility that any considerable bias or uncertainty is introduced by this methodological choice. However, we impose caps on these moderating variables at the 95th percentile at which they were observed in the historical data to prevent extrapolation of the marginal effects outside the range in which the regressions were estimated. This is a conservative choice that limits the magnitude of our damage projections.

Time series of primary climate variables and moderating climate variables are then combined with estimates of the empirical model parameters to evaluate the regression coefficients in equation ( 10 ), producing a time series of annual GRPpc growth-rate reductions for a given emission scenario, climate model and set of empirical model parameters. The resulting time series of growth-rate impacts reflects those occurring owing to future climate change. By contrast, a future scenario with no climate change would be one in which climate variables do not change (other than with random year-to-year fluctuations) and hence the time-averaged evaluation of equation ( 10 ) would be zero. Our approach therefore implicitly compares the future climate-change scenario to this no-climate-change baseline scenario.

The time series of growth-rate impacts owing to future climate change in region r and year y , δ r , y , are then added to the future baseline growth rates, π r , y (in log-diff form), obtained from the SSP2 scenario to yield trajectories of damaged GRPpc growth rates, ρ r , y . These trajectories are aggregated over time to estimate the future trajectory of GRPpc with future climate impacts:

in which GRPpc r , y =2020 is the initial log level of GRPpc. We begin damage estimates in 2020 to reflect the damages occurring since the end of the period for which we estimate the empirical models (1979–2019) and to match the timing of mitigation-cost estimates from most IAMs (see below).

For each emission scenario, this procedure is repeated 1,000 times while randomly sampling from the selection of climate models, the selection of empirical models with different numbers of lags (shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) and bootstrapped estimates of the regression parameters. The result is an ensemble of future GRPpc trajectories that reflect uncertainty from both physical climate change and the structural and sampling uncertainty of the empirical models.

Estimates of mitigation costs

We obtain IPCC estimates of the aggregate costs of emission mitigation from the AR6 Scenario Explorer and Database hosted by IIASA 23 . Specifically, we search the AR6 Scenarios Database World v1.1 for IAMs that provided estimates of global GDP and population under both a SSP2 baseline and a SSP2-RCP2.6 scenario to maintain consistency with the socio-economic and emission scenarios of the climate damage projections. We find five IAMs that provide data for these scenarios, namely, MESSAGE-GLOBIOM 1.0, REMIND-MAgPIE 1.5, AIM/GCE 2.0, GCAM 4.2 and WITCH-GLOBIOM 3.1. Of these five IAMs, we use the results only from the first three that passed the IPCC vetting procedure for reproducing historical emission and climate trajectories. We then estimate global mitigation costs as the percentage difference in global per capita GDP between the SSP2 baseline and the SSP2-RCP2.6 emission scenario. In the case of one of these IAMs, estimates of mitigation costs begin in 2020, whereas in the case of two others, mitigation costs begin in 2010. The mitigation cost estimates before 2020 in these two IAMs are mostly negligible, and our choice to begin comparison with damage estimates in 2020 is conservative with respect to the relative weight of climate damages compared with mitigation costs for these two IAMs.

Data availability

Data on economic production and ERA-5 climate data are publicly available at https://doi.org/10.5281/zenodo.4681306 (ref. 62 ) and https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 , respectively. Data on mitigation costs are publicly available at https://data.ene.iiasa.ac.at/ar6/#/downloads . Processed climate and economic data, as well as all other necessary data for reproduction of the results, are available at the public repository https://doi.org/10.5281/zenodo.10562951 (ref. 63 ).

Code availability

All code necessary for reproduction of the results is available at the public repository https://doi.org/10.5281/zenodo.10562951 (ref. 63 ).

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Acknowledgements

We gratefully acknowledge financing from the Volkswagen Foundation and the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH on behalf of the Government of the Federal Republic of Germany and Federal Ministry for Economic Cooperation and Development (BMZ).

Open access funding provided by Potsdam-Institut für Klimafolgenforschung (PIK) e.V.

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All authors contributed to the design of the analysis. M.K. conducted the analysis and produced the figures. All authors contributed to the interpretation and presentation of the results. M.K. and L.W. wrote the manuscript.

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Extended data figures and tables

Extended data fig. 1 constraining the persistence of historical climate impacts on economic growth rates..

The results of a panel-based fixed-effects distributed lag model for the effects of annual mean temperature ( a ), daily temperature variability ( b ), total annual precipitation ( c ), the number of wet days ( d ) and extreme daily precipitation ( e ) on sub-national economic growth rates. Point estimates show the effects of a 1 °C or one standard deviation increase (for temperature and precipitation variables, respectively) at the lower quartile, median and upper quartile of the relevant moderating variable (green, orange and purple, respectively) at different lagged periods after the initial shock (note that these are not cumulative effects). Climate variables are used in their first-differenced form (see main text for discussion) and the moderating climate variables are the annual mean temperature, seasonal temperature difference, total annual precipitation, number of wet days and annual mean temperature, respectively, in panels a – e (see Methods for further discussion). Error bars show the 95% confidence intervals having clustered standard errors by region. The within-region R 2 , Bayesian and Akaike information criteria for the model are shown at the top of the figure. This figure shows results with ten lags for each variable to demonstrate the observed levels of persistence, but our preferred specifications remove later lags based on the statistical significance of terms shown above and the information criteria shown in Extended Data Fig. 2 . The resulting models without later lags are shown in Supplementary Figs. 1 – 3 .

Extended Data Fig. 2 Incremental lag-selection procedure using information criteria and within-region R 2 .

Starting from a panel-based fixed-effects distributed lag model estimating the effects of climate on economic growth using the real historical data (as in equation ( 4 )) with ten lags for all climate variables (as shown in Extended Data Fig. 1 ), lags are incrementally removed for one climate variable at a time. The resulting Bayesian and Akaike information criteria are shown in a – e and f – j , respectively, and the within-region R 2 and number of observations in k – o and p – t , respectively. Different rows show the results when removing lags from different climate variables, ordered from top to bottom as annual mean temperature, daily temperature variability, total annual precipitation, the number of wet days and extreme annual precipitation. Information criteria show minima at approximately four lags for precipitation variables and ten to eight for temperature variables, indicating that including these numbers of lags does not lead to overfitting. See Supplementary Table 1 for an assessment using information criteria to determine whether including further climate variables causes overfitting.

Extended Data Fig. 3 Damages in our preferred specification that provides a robust lower bound on the persistence of climate impacts on economic growth versus damages in specifications of pure growth or pure level effects.

Estimates of future damages as shown in Fig. 1 but under the emission scenario RCP8.5 for three separate empirical specifications: in orange our preferred specification, which provides an empirical lower bound on the persistence of climate impacts on economic growth rates while avoiding assumptions of infinite persistence (see main text for further discussion); in purple a specification of ‘pure growth effects’ in which the first difference of climate variables is not taken and no lagged climate variables are included (the baseline specification of ref. 2 ); and in pink a specification of ‘pure level effects’ in which the first difference of climate variables is taken but no lagged terms are included.

Extended Data Fig. 4 Climate changes in different variables as a function of historical interannual variability.

Changes in each climate variable of interest from 1979–2019 to 2035–2065 under the high-emission scenario SSP5-RCP8.5, expressed as a percentage of the historical variability of each measure. Historical variability is estimated as the standard deviation of each detrended climate variable over the period 1979–2019 during which the empirical models were identified (detrending is appropriate because of the inclusion of region-specific linear time trends in the empirical models). See Supplementary Fig. 13 for changes expressed in standard units. Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Extended Data Fig. 5 Contribution of different climate variables to overall committed damages.

a , Climate damages in 2049 when using empirical models that account for all climate variables, changes in annual mean temperature only or changes in both annual mean temperature and one other climate variable (daily temperature variability, total annual precipitation, the number of wet days and extreme daily precipitation, respectively). b , The cumulative marginal effects of an increase in annual mean temperature of 1 °C, at different baseline temperatures, estimated from empirical models including all climate variables or annual mean temperature only. Estimates and uncertainty bars represent the median and 95% confidence intervals obtained from 1,000 block-bootstrap resamples from each of three different empirical models using eight, nine or ten lags of temperature terms.

Extended Data Fig. 6 The difference in committed damages between the upper and lower quartiles of countries when ranked by GDP and cumulative historical emissions.

Quartiles are defined using a population weighting, as are the average committed damages across each quartile group. The violin plots indicate the distribution of differences between quartiles across the two extreme emission scenarios (RCP2.6 and RCP8.5) and the uncertainty sampling procedure outlined in Methods , which accounts for uncertainty arising from the choice of lags in the empirical models, uncertainty in the empirical model parameter estimates, as well as the climate model projections. Bars indicate the median, as well as the 10th and 90th percentiles and upper and lower sixths of the distribution reflecting the very likely and likely ranges following the likelihood classification adopted by the IPCC.

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Kotz, M., Levermann, A. & Wenz, L. The economic commitment of climate change. Nature 628 , 551–557 (2024). https://doi.org/10.1038/s41586-024-07219-0

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Essay: A letter from Manipur

Two months of life in a town besieged by strife is a constant reminder of the brevity and precariousness of life.

It has been two months since the ethnic civil war started in Manipur between the majority Meitei community and the Kuki/Zo tribes, who comprise roughly 16 percent of the population.

Imphal: Unidentified miscreants torch two houses in retaliation against he killing of nine civilians by Kuki militants, in Manipur, Thursday, Jun 15, 2023. (PTI)

Two months of life in a town besieged by strife is a constant reminder of religious teachings about the brevity and precariousness of life. Human life is like the dew that disappears as the sun rises, the Bible tells us. It is like a blossoming flower that wilts in the evening. Yes, we know, death will come to everyone. And yet, it is tough to entertain the thought of dying, especially dying violently.

The lurking nearness of violence and death was revealed early on in the conflict. On the evening of May 3, a group of neighbours was in our house for a time of Christian fellowship that’s held in rotation every Wednesday. We are in the heart of Lamka town and notwithstanding what is happening around us, we try to act as normal as possible. As the fellowship wound up and people were leaving, we were jolted by a sharp piercing sound. I tried to make light of it by saying that some slingshots must have hit the tin roof. But when we returned after seeing off our guests, my wife saw a splinter of material from the ceiling on the floor. We checked and recovered a spent bullet. It was still hot. A stray bullet had hit our tin roof, penetrated the ceiling and struck the tile floors below. Suddenly, it was clear that the war is indeed upon us.

Armed Meitei community members keep a watch from a bunker on rival tribal Kuki community bunkers at a de facto frontline which dissects the area into two ethnic zones in Sugnu, Manipur on Wednesday, June 21, 2023. (Altaf Qadri/AP)

Since then, we have been living a life of uncertainty, fear and grief. We get up in the morning to the hoo-hah sounds of local volunteers doing their PT runs. While brewing tea, we sniff the air for signs of tension or strife in the neighbourhood. Almost as an unconscious reflex, we check our phones for WhatsApp messages. There’s nothing to see because the internet has been off since the beginning of the troubles. We turn to the TV and scroll through the news channels. That’s what we do most days. It’s impossible to focus on anything else.

I have piles of books and papers waiting, unread and unmarked. My PhD research work too is certainly not writing itself. WhatsApp is off, the kids are away, and the colleges are closed. We’re told to stay indoors except when called to sit in dharna or assigned some relief-related work. What a perfect time for a life of the mind! Yet, when I pick up a book, I can hardly make it past the second page before my mind starts to roam and I end up not knowing how far I have wandered. I think of Amartya Sen, of all people. I recall reading his memoir where he mentions how he could do his reading and writing undisturbed even while waiting for a train at a bustling station. I feel guilty and irritable. My itchy fingers reach for the TV remote yet again.

Apart from the sale of essential food and petrol on the black market, cable TV is the one business that has seen a revival since the clashes began. With broadband internet in most homes and smartphones in every hand, people had forgotten about cable TV. Then, the violence started and the Internet was shut down. Now, cable operators are struggling to cater to demands for reinstallation.

On a particular day, the big news on most channels was the “Pawar-play” in Maharashtra. Talking heads shouted at each other, like always. Earlier, it was the ministry formation in Karnataka. Watching from this besieged settlement in Manipur, all that is non-news. The one regional news channel called Northeast Live, which is Guwahati-based, is as deferential to power as any other and the quality of reportage leaves much to be desired. Yet, it is the only one we have. The channel continuously runs a ticker tape at the bottom of the screen highlighting the news as it comes in. This is where our eyes are focused.

Soldiers inspect the site of a car bomb explosion in Kwakta, some 50 kilometers from Imphal, Manipur, on Manipur, Thursday, June 22, 2023. (AP)

Then there are the local channels. Channel 1 is for periodic local news broadcasts with Christian gospel songs in between. Channel 2 is for romantic love songs. Channel 3 features selected TV debates on the conflict downloaded from various outlets, incident reports on the present conflict, and victim testimonies.

Some people may think victim testimonies are provocative. Some say the way forward is to forgive and forget. Try telling that to people who saw their family members killed or raped, their homes built with their life savings turned to ashes. Listening to survivors and victims helps us understand the conflict from their point of view and to connect with them as fellow humans. It turns distant anger into proximate grief. Sometimes, it transforms hatred into sadness. It also helps the survivors, I think, cope and maybe find a way to heal.

I listen to a girl recounting how her brother and mother were beaten to death as they tried to flee to an army camp. She was calm. She did not cry even once. It is me, the listener, who is struggling to contain his tears.

I watch an elderly man and his wife speak of how they ran away from their village to the jungles to escape the attacking mob. They were in the jungles for four nights before they were rescued by the army. He was speaking slowly, in a monotone, and I am not sure if his staring eyes can see. He looked exasperated, most of all. Responding to a question, he said they had to kill their animals and their dog before fleeing. He had to kill his dog because had he taken the dog along, it would bark at the attackers, thereby exposing their location. At that point, I felt the room become very still. A lump formed in my throat. I turned the TV off.

An armed tribal Kuki keeps a watch on rival Meitei community bunkers along a de facto frontline which dissects the area into two ethnic zones in Churachandpur, Manipur on Tuesday, June 20, 2023. (AP)

We don’t know how long we will remain like this. Two months have passed, the physical separation is complete, and the warring parties are not even ready to sit together in a room, let alone talk peace.

For now, our entire thought is focused on physical security. Even when there is no direct violence, the possibility is all too real, all too pervasive. It is this possibility of society degenerating so quickly into a free for all, much like the Hobbesian state of nature, that is most tormenting. Is our front gate strong enough, high enough? Why didn’t we foresee this situation when we built this house, with very low windows? Why didn’t we think of an escape gate at the back? Gas cylinders are reminders of those loud bursts from inside the kitchen as houses are torched. Roads are seen only as attack routes or escape routes. Oh, by the way, did any shootings happen today? Any deaths on their side? Or on our side?

Mundane matters like going to school, travelling to the Imphal airport, or going on a family picnic seem like unreal pictures in a forgotten album, snapshots from a lost time.

Thangkhanlal Ngaihte teaches political science at Churachandpur College, Lamka, Manipur.

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Climate Change Essay

500+ words essay on climate change.

Climate change is a major global challenge today, and the world is becoming more vulnerable to this change. Climate change refers to the changes in Earth’s climate condition. It describes the changes in the atmosphere which have taken place over a period ranging from decades to millions of years. A recent report from the United Nations predicted that the average global temperature could increase by 6˚ Celsius at the end of the century. Climate change has an adverse effect on the environment and ecosystem. With the help of this essay, students will get to know the causes and effects of climate change and possible solutions. Also, they will be able to write essays on similar topics and can boost their writing skills.

What Causes Climate Change?

The Earth’s climate has always changed and evolved. Some of these changes have been due to natural causes such as volcanic eruptions, floods, forest fires etc., but quite a few of them are due to human activities. Human activities such as deforestation, burning fossil fuels, farming livestock etc., generate an enormous amount of greenhouse gases. This results in the greenhouse effect and global warming which are the major causes of climate change.

Effects of Climate Change

If the current situation of climate change continues in a similar manner, then it will impact all forms of life on the earth. The earth’s temperature will rise, the monsoon patterns will change, sea levels will rise, and storms, volcanic eruptions and natural disasters will occur frequently. The biological and ecological balance of the earth will get disturbed. The environment will get polluted and humans will not be able to get fresh air to breathe and fresh water to drink. Life on earth will come to an end.

Steps to be Taken to Reduce Climate Change

The Government of India has taken many measures to improve the dire situation of Climate Change. The Ministry of Environment and Forests is the nodal agency for climate change issues in India. It has initiated several climate-friendly measures, particularly in the area of renewable energy. India took several steps and policy initiatives to create awareness about climate change and help capacity building for adaptation measures. It has initiated a “Green India” programme under which various trees are planted to make the forest land more green and fertile.

We need to follow the path of sustainable development to effectively address the concerns of climate change. We need to minimise the use of fossil fuels, which is the major cause of global warming. We must adopt alternative sources of energy, such as hydropower, solar and wind energy to make a progressive transition to clean energy. Mahatma Gandhi said that “Earth provides enough to satisfy every man’s need, but not any man’s greed”. With this view, we must remodel our outlook and achieve the goal of sustainable development. By adopting clean technologies, equitable distribution of resources and addressing the issues of equity and justice, we can make our developmental process more harmonious with nature.

We hope students liked this essay on Climate Change and gathered useful information on this topic so that they can write essays in their own words. To get more study material related to the CBSE, ICSE, State Board and Competitive exams, keep visiting the BYJU’S website.

Frequently Asked Questions on climate change Essay

What are the reasons for climate change.

1. Deforestation 2. Excessive usage of fossil fuels 3. Water, Soil pollution 4. Plastic and other non-biodegradable waste 5. Wildlife and nature extinction

How can we save this climate change situation?

1. Avoid over usage of natural resources 2. Do not use or buy items made from animals 3. Avoid plastic usage and pollution

Are there any natural causes for climate change?

Yes, some of the natural causes for climate change are: 1. Solar variations 2. Volcanic eruption and tsunamis 3. Earth’s orbital changes

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More From Forbes

Biden’s climate emergency: green policies on steroids.

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Allegory (Apocalypse). Found in the Collection of Art History Museum, Vienne. (Photo by Fine Art ... [+] Images/Heritage Images/Getty Images)

According to a Bloomberg report last week, White House officials have renewed discussions about declaring a national “climate emergency”. The intent is not new. Six days after President Biden’s inauguration, the then newly minted Senate Majority Leader Chuck Schumer called for the president to declare an emergency over the “climate crisis.” In 2022, the administration considered a similar emergency declaration after negotiations on “clean energy” legislation had stalled.

As the US presidential election campaigning heats up, Biden’s poll numbers are flagging across the battleground states. According to Matt Walsh of the Daily Wire, it may seem to White House strategists “a really good idea to essentially go to war with the weather” to rally the troops. Or is this too cynical a view? With the incessant coverage of extreme weather in the mainstream media, might not President Biden and his advisors be convinced of a true “climate emergency”?

The Regulatory Onslaught on Fossil Fuels Since January 2020

Since its inception, the Biden Administration has done just about everything to wage a regulatory onslaught on U.S. oil and gas—coal of course is beyond the pale. In an interview with The Weather Channel in August, President Biden said that “practically speaking” he had already declared a climate emergency. He had a point. On attaining office, the president immediately unleashed a series of executive orders to reverse his predecessor’s strategy of “ energy independence ”. In short order, he revoked permits for the Keystone $7 billion XL oil pipeline, suspended oil leasing in Alaska, halted oil and gas leases on federal land, and even invoked the Endangered Species Act to block energy resource development on private lands in the West.

Thomas Pyle of the Institute for Energy Research published a detailed list of over 200 actions pursued by the Biden administration “deliberately designed to make it harder to produce [conventional] energy here in America.” Francis Menton of the Manhattan Contrarian , in commenting on the list, said that “the sheer number of efforts to restrict, hamper, harass and extort fossil fuel producers is breathtaking.”

There is no easing in the determination to continue the regulatory onslaught on oil and gas. In what is effectively seen as the Environmental Protection Agency’s “plan to ration electricity”, the Wall Street Journal wryly observed on Friday: “The Biden Administration’s regulations are coming so fast and furious that its hard even to keep track, but we are trying.” The Journal said the EPA “proposed its latest doozy—rules that will effectively force coal plants to shut down while banning new natural-gas plants.”

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The EPA’s tightening disposal standards for mercury, wastewater and ash will make it impossible for coal and natural gas-fuelled power stations to operate unless carbon capture and sequestration technologies become economical. The agency would have us believe that the plunging costs of wind, solar and battery technologies will rapidly displace demand for fossil fuels which currently account for 82% of the world’s primary energy supply , according to BP’s latest annual statistical bulletin. According to Matt Walsh of the Daily Wire, it may seem to White House strategists “a really good idea to essentially go to war with the weather” to rally the troops.

The Nature of ‘Emergencies’

When a danger, created by internal or external threats, puts citizens at risk, governments must respond decisively and take action that would be considered excessive during normal times. In an emergency, democratic checks and balances can hamper the government when they need to act quickly. The use of special powers in response to abnormally perilous situations has existed as long as nations have. However, it can also increase the opportunities for abuses of power as democratic constraints on executive power are weakened.

According to the American Heritage Dictionary, an emergency is “a serious situation or occurrence that happens unexpectedly and demands immediate action”. Since dire failed predictions of climate catastrophe have been with us for over half a century, it is incorrect to cast the climate as being in a state of emergency.

But beyond mere etymological incorrectness, the more concerning aspect of proclaiming national emergency is that it excuses executive authority from the constraints of the normal rule of law. It evades public opinion, the checks and balances of legislation, and the censure of constitutional impeachment.

An emergency declaration allows the president to access funds from the Treasury even for purposes that Congress might have specifically rejected, taking away the House’s “power of the purse.” Thus, accountability to the people for the expenditure of their money, the Constitution’s safeguard for imposing popular will on government, is made redundant.

As Greg Weiner reminds us, James Madison’s fifty-eighth of The Federalist Papers states that the House’s “power over the purse may, in fact, be regarded as the most complete and effectual weapon, with which any constitution can arm the immediate representatives of the people, for obtaining a redress of every grievance, and for carrying into effect every just and salutary measure.”

According to the Congressional Records Office, the U.S. National Emergency Act—enacted in 1976—was explicitly proclaimed at least 30 times since it was first used by the Carter administration. The prolific use of emergency proclamations by presidential fiat makes it more likely that it ends up protecting abuses of executive authority rather than in any prudential use of measures to attend to real emergencies.

Biden’s Climate Emergency Will Wreak Economic Carnage

Already with a record number of executive orders and regulatory overreach by agencies such as the EPA, a climate emergency declaration will allow the Biden administration to seize even more powers without restraint from the House’s control over the appropriations process. Research by the Brennan Center for Justice catalogs 123 statutory authorities that become available to the president when he declares a national emergency. According to the earlier cited Bloomberg report , a proclaimed “climate emergency” could be used “to curtail crude exports, suspend offshore drilling and curb greenhouse gas emissions…and throttle the movement of oil and gas on pipelines, ships and trains.”

Alongside this veto over the extraction, processing and use fossil fuels in the country, the deluge of subsidies and mandates to support favoured green industries such as solar, wind, electric vehicles and battery technologies will grow even more. According to the U.S. Energy Information Administration, subsidies for renewable energy producers more than doubled between 2016 and 2022, forming nearly half of all federal energy-related support in that period.

The hidden costs involved with the countless mandates to force “green” initiatives on the public would be a multiple of this. To this should be added the estimated $1 trillion in renewable energy subsidies over the next 10 years. These subsidies, via investment tax credits and other instruments mainly based on debt, will be made available in the egregiously misnamed Inflation Reduction Act (IRA).

In an email to Bloomberg, White House spokesman Angelo Fernandez Hernandez said that President Biden has “delivered on the most ambitious climate agenda in history…[he] has treated the climate crisis as an emergency since day one and will continue to build a clean energy future that lowers utility bills, creates good-paying union jobs, makes our economy the envy of the world and prioritizes communities that for too long have been left behind.”

Compare that official response to that of Forbes Media chairman and editor-in-chief Steve Forbes. Declaring that a national climate emergency would “wreck” the U.S. economy, he said “You’re going to pay for it with an even more troubled economy,” adding that Americans will suffer the consequences of “higher energy prices.”

The reader will decide which of the two statements is closer to the mark. But among the more cynical, it may seem that the president is being rather practical. President Biden—and his handlers—are aiming to save his chances for the November election by appealing to the youth convinced of the climate apocalypse and the leftist billionaires who can’t seem to throw enough money for the cause.

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Manipur Violence Explained: A Year On, What Must Be Done To Restore Peace And Trust

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Hot Oceans Worsened Dubai’s Dramatic Flooding, Scientists Say

An international team of researchers found that heavy rains had intensified in the region, though they couldn’t say for sure how much climate change was responsible.

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Trucks under water with a bridge in the background.

By Raymond Zhong

Scenes of flood-ravaged neighborhoods in one of the planet’s driest regions stunned the world this month. Heavy rains in the United Arab Emirates and Oman submerged cars, clogged highways and killed at least 21 people. Flights out of Dubai’s airport, a major global hub, were severely disrupted.

The downpours weren’t a total surprise — forecasters had anticipated the storms several days earlier and issued warnings. But they were certainly unusual.

Here’s what to know.

Heavy rain there is rare, but not unheard-of.

On average, the Arabian Peninsula receives a scant few inches of rain a year, although scientists have found that a sizable chunk of that precipitation falls in infrequent but severe bursts, not as periodic showers. These rains often come during El Niño conditions like the ones the world is experiencing now.

U.A.E. officials said the 24-hour rain total on April 16 was the country’s largest since records there began in 1949 . And parts of the nation had already experienced an earlier round of thunderstorms in March.

Oman, with its coastline on the Arabian Sea, is also vulnerable to tropical cyclones. Past storms there have brought torrential rain, powerful winds and mudslides, causing extensive damage.

Global warming is projected to intensify downpours.

Stronger storms are a key consequence of human-caused global warming. As the atmosphere gets hotter, it can hold more moisture, which can eventually make its way down to the earth as rain or snow.

But that doesn’t mean rainfall patterns are changing in precisely the same way across every part of the globe.

In their latest assessment of climate research , scientists convened by the United Nations found there wasn’t enough data to have firm conclusions about rainfall trends in the Arabian Peninsula and how climate change was affecting them. The researchers said, however, that if global warming were to be allowed to continue worsening in the coming decades, extreme downpours in the region would quite likely become more intense and more frequent.

Hot oceans are a big factor.

An international team of scientists has made a first attempt at estimating the extent to which climate change may have contributed to April’s storms. The researchers didn’t manage to pin down the connection precisely, though in their analysis, they did highlight one known driver of heavy rain in the region: above-normal ocean temperatures.

Large parts of the Indian, Pacific and Atlantic Oceans have been hotter than usual recently, in part because of El Niño and other natural weather cycles, and in part because of human-induced warming .

When looking only at El Niño years, the scientists estimated that storm events as infrequent as this month’s delivered 10 percent to 40 percent more rain to the region than they would in a world that hadn’t been warmed by human activities. They cautioned, however, that these estimates were highly uncertain.

“Rainfall, in general, is getting more extreme,” said Mansour Almazroui, a climate scientist at King Abdulaziz University in Jeddah, Saudi Arabia, and one of the researchers who contributed to the analysis.

The analysis was conducted by scientists affiliated with World Weather Attribution, a research collaboration that studies extreme weather events shortly after they occur. Their findings about this month’s rains haven’t yet been peer reviewed, but are based on standardized methods .

The role of cloud seeding isn’t clear.

The U.A.E. has for decades worked to increase rainfall and boost water supplies by seeding clouds. Essentially, this involves shooting particles into clouds to encourage the moisture to gather into larger, heavier droplets, ones that are more likely to fall as rain or snow.

Cloud seeding and other rain-enhancement methods have been tried around the world, including in Australia, China, India, Israel, South Africa and the United States. Studies have found that these operations can, at best, affect precipitation modestly — enough to turn a downpour into a bigger downpour, but probably not a drizzle into a deluge.

Still, experts said pinning down how much seeding might have contributed to this month’s storms would require detailed study.

“In general, it is quite a challenge to assess the impact of seeding,” said Luca Delle Monache, a climate scientist at the Scripps Institution of Oceanography in La Jolla, Calif. Dr. Delle Monache has been leading efforts to use artificial intelligence to improve the U.A.E.’s rain-enhancement program.

An official with the U.A.E.’s National Center of Meteorology, Omar Al Yazeedi, told news outlets that the agency didn’t conduct any seeding during the latest storms. His statements didn’t make clear, however, whether that was also true in the hours or days before.

Mr. Al Yazeedi didn’t respond to emailed questions from The New York Times, and Adel Kamal, a spokesman for the center, didn’t have further comment.

Cities in dry places just aren’t designed for floods.

Wherever it happens, flooding isn’t just a matter of how much rain comes down. It’s also about what happens to all that water once it’s on the ground — most critically, in the places people live.

Cities in arid regions often aren’t designed to drain very effectively. In these areas, paved surfaces block rain from seeping into the earth below, forcing it into drainage systems that can easily become overwhelmed.

One recent study of Sharjah , the capital of the third-largest emirate in the U.A.E., found that the city’s rapid growth over the past half-century had made it vulnerable to flooding at far lower levels of rain than before.

Omnia Al Desoukie contributed reporting.

Raymond Zhong reports on climate and environmental issues for The Times. More about Raymond Zhong

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