More frequent global extreme heat events prompt behavioral adaptations, such as reducing outdoor activities to relieve potential distress. The emergence of innovative daily life services in cities offers new avenues for implementing such adaptive strategies. Here we investigate whether urban residents augment food delivery consumption as an immediate response to hot weather in China. Analyzing extensive food delivery service data across 100 Chinese cities from 2017 to 2023, we observe a significant surge in lunchtime orders, exceeding 12.6

As a major contributor to carbon emissions, the transportation sector faces immense pressure to align with China's carbon peak and neutrality goals. Technological progress is a crucial strategy for achieving these goals. However, rebound effects can make technological progress a double-edged sword in terms of reducing emissions. A deep understanding of the carbon rebound effect (CRE) in the transportation sector is crucial for fully leveraging the role of technological progress on carbon reduction. This study uses nonparametric frontier methods to calculate and analyze the CRE of the transportation sector related to technological progress in 30 provinces of China from 2006 to 2021 for the first time. The relevant findings are threefold. (1) During the sample period, the estimated CRE ranged from 20 % to 210 %, averaging 69.19 %. Half of provinces exhibited backfire effects, that is, rebound emissions exceeded the low-carbon gains. (2) CRE exhibits fluctuations over time and is significantly affected by economic policy shocks. Technological progress is a significant driver of transportation carbon emissions reduction. (3) CRE varies considerably across regions, with the eastern coastal provinces exhibiting the lowest CRE, averaging 57.96 %. The central regions followed with an average of 81.54 %, while the western regions had the highest CRE of 111.85 %. This study has crucial implications for enabling policymakers to better understand CRE in the transportation sector and strategically develop subsequent policies that are specifically tailored to regional conditions.

Evidence shows a continuing increase in the frequency and severity of global heatwaves1,2, raising concerns about the future impacts of climate change and the associated socioeconomic costs3,4. Here we develop a disaster footprint analytical framework by integrating climate, epidemiological and hybrid input-output and computable general equilibrium global trade models to estimate the midcentury socioeconomic impacts of heat stress. We consider health costs related to heat exposure, the value of heat-induced labour productivity loss and indirect losses due to economic disruptions cascading through supply chains. Here we show that the global annual incremental gross domestic product loss increases exponentially from 0.03 ± 0.01 (SSP 245)-0.05 ± 0.03 (SSP 585) percentage points during 2030-2040 to 0.05 ± 0.01-0.15 ± 0.04 percentage points during 2050-2060. By 2060, the expected global economic losses reach a total of 0.6-4.6% with losses attributed to health loss (37-45%), labour productivity loss (18-37%) and indirect loss (12-43%) under different shared socioeconomic pathways. Small- and medium-sized developing countries suffer disproportionately from higher health loss in South-Central Africa (2.1 to 4.0 times above global average) and labour productivity loss in West Africa and Southeast Asia (2.0-3.3 times above global average). The supply-chain disruption effects are much more widespread with strong hit to those manufacturing-heavy countries such as China and the USA, leading to soaring economic losses of 2.7 ± 0.7% and 1.8 ± 0.5%, respectively.

The influence of manufacturing agglomeration on economic efficiency is substantial, yet its effects on Green Total Factor Productivity (GTFP) are still subject to debate. It is vital to comprehend the relationship between these two factors to craft effective sustainable development policies. Employing the newly developed partially linear functional-coefficient panel data approach, this study examines the nonlinear relationship between manufacturing agglomeration and GTFP, with a comprehensive consideration of the heterogeneity inherent to city geographical attributes and urban scale. Our results reveal that manufacturing agglomeration, on average, fosters GTFP, while the positive effect consists of two opposite components. Agglomeration promotes the diffusion of technology at any stage of urban development, but it can lead to congestion effects in well-developed economies, thereby diminishing efficiency. Our nonlinear approach indicates the turning points of the negative impact. Additionally, the heterogeneity of the relationship between agglomeration and GTFP across cities with varied locations and scales suggested that strategies for manufacturing agglomeration and green development should be tailor-made for individual city types.

Regional conflicts have become prominent in triggering shocks on supply chains and cascade effects on resource management. Reliable assessments of the cascading pattern of production resources among sectors globally are missing. Here, we modeled global multisectoral production losses and the cascading pattern of a grain supply shock in the Russia-Ukraine regional conflict by utilizing a geographic input-output approach. We find that the most cascading losses emerged in the textile (17.04 % +/- 0.72 %, 95 % confidence intervals) and food-processing sectors (16.85 % +/- 0.5 %). The shock propagated in a "grain-processed food/livestock" direct chain and a "light manufacturing-heavy manufacturing/textile" indirect chain. Prolonged conflict and disrupted resource allocation decreased the efficiency of production recovery in low-income countries and amplified inequality of production resources. Our approach presents a quantitative framework for unexpected supply chain shocks. The findings support the case for production aid to low-income countries and circular supply chains for sustainable development.

Given that it was a once-in-a-century emergency event, the confinement measures related to the coronavirus disease 2019 (COVID-19) pandemic caused diverse disruptions and changes in life and work patterns. These changes significantly affected water consumption both during and after the pandemic, with direct and indirect consequences on biodiversity. However, there has been a lack of holistic evaluation of these responses. Here, we propose a novel framework to study the impacts of this unique global emergency event by embedding an environmentally extended supply-constrained global multi-regional input-output model (MRIO) into the drivers-pressure-state-impact-response (DPSIR) framework. This framework allowed us to develop scenarios related to COVID-19 confinement measures to quantify country-sector-specific changes in freshwater consumption and the associated changes in biodiversity for the period of 2020-2025. The results suggest progressively diminishing impacts due to the implementation of COVID-19 vaccines and the socio-economic system's self-adjustment to the new normal. In 2020, the confinement measures were estimated to decrease global water consumption by about 5.7% on average across all scenarios when compared with the baseline level with no confinement measures. Further, such a decrease is estimated to lead to a reduction of around 5% in the related pressure on biodiversity. Given the interdependencies and interactions across global supply chains, even those countries and sectors that were not directly affected by the COVID-19 shocks experienced significant impacts: Our results indicate that the supply chain propagations contributed to 79% of the total estimated decrease in water consumption and 84% of the reduction in biodiversity loss on average. Our study demonstrates that the MRIO-enhanced DSPIR framework can help quantify resource pressures and the resultant environmental impacts across supply chains when facing a global emergency event. Further, we recommend the development of more locally based water conservation measures-to mitigate the effects of trade disruptions-and the explicit inclusion of water resources in post-pandemic recovery schemes. In addition, innovations that help conserve natural resources are essential for maintaining environmental gains in the post-pandemic world.

Chinese cities are core in the national carbon mitigation and largely affect global decarbonisation initiatives, yet disparities between cities challenge country-wide progress. Low-carbon transition should preferably lead to a convergence of both equity and mitigation targets among cities. Inter-city supply chains that link the production and consumption of cities are a factor in shaping inequality and mitigation but less considered aggregately. Here, we modelled supply chains of 309 Chinese cities for 2012 to quantify carbon footprint inequality, as well as explored a leverage opportunity to achieve an inclusive low-carbon transition. We revealed significant carbon inequalities: the 10 richest cities in China have per capita carbon footprints comparable to the US level, while half of the Chinese cities sit below the global average. Inter-city supply chains in China, which are associated with 80% of carbon emissions, imply substantial carbon leakage risks and also contribute to socioeconomic disparities. However, the significant carbon inequality implies a leveraging opportunity that substantial mitigation can be achieved by 32 super-emitting cities. If the super-emitting cities adopt their differentiated mitigation pathway based on affluence, industrial structure, and role of supply chains, up to 1.4 Gt carbon quota can be created, raising 30% of the projected carbon quota to carbon peak. The additional carbon quota allows the average living standard of the other 60% of Chinese people to reach an upper-middle-income level, highlighting collaborative mechanism at the city level has a great potential to lead to a convergence of both equity and mitigation targets.

The COVID-19 lockdowns have transitioned to a new normal and triggered commodity supply disruption and trade uncertainty, yet little is known about the seafood trade resilience of developing and developed countries amid pandemic-related shocks. Here, employing a newly developed geographical transition-net model, we simulate a set of idealized lockdown scenarios in a real-world seafood network. The results show that (1) even if restrictions from regions with high strictness policies were eventually lifted globally at the end of 2022, the pandemic-induced disruption will continue to affect global seafood trade until 2030, and the annual growth rate of the global seafood market would be around 1% lower than that during 2006–2019; (2) Due to the continued high level of stringency in China in 2022 and the soaring demand of seafood in the developed countries in the post-COVID-19 era, developed countries are increasingly reliant on their intra-regional trade until 2030; (3) The global seafood supply chains will magnify export losses beyond the direct effects of COVID-19, and there would be 17 to 57 million people in the developing countries in 2030 facing seafood supply shortage. The new long-term challenge is to call for the multilateral cooperation of major exporters for global seafood trade recovery. Our study provides a new perspective to evaluate the economic impact of COVID-19 as well as the cascading effect caused by the supply-chain linkages in the global seafood system.

Effectively reducing transportation carbon emissions is greatly significant to achieve the carbon peaking and neutral goals of China. On the basis of considering regional technology heterogeneity, we employ the parametric metafrontier approach to analyze the carbon emission performance and reduction potential of the transportation sector in China. Then, we further decompose the emission reduction potential's contributors into removing management inefficiencies and filling technology gaps. The estimated potential carbon emission reductions from transportation sector in China are 12.3 million tons, accounting for 8.4 % of the annual transportation carbon emissions. Specifically, the eastern regions, especially Shandong, Shanghai, and Liaoning have the greatest carbon emission reduction potential; while Qinghai, Jiangxi, and Ningxia have the smallest potential. As the major contributors to the potential emission reductions, filling technology gaps and removing management inefficiencies account for 57.5 % and 42.5 % of the total potential, respectively. Moreover, removing management inefficiencies dominate for the eastern region and filling technology gaps for the central and western regions. Finally, we provide provincial-specific emission mitigation strategies based on the identification of the reduction potential and its contributors. Our policy implications help decision-makers to facilitate the low-carbon development of transportation sector.

Ensuring a more equitable distribution of vaccines worldwide is an effective strategy to control global pandemics and support economic recovery. We analyze the socioeconomic effects - defined as health gains, lockdown-easing effect, and supply-chain rebuilding benefit - of a set of idealized COVID-19 vaccine distribution scenarios. We find that an equitable vaccine distribution across the world would increase global economic benefits by 11.7% ($950 billion per year), compared to a scenario focusing on vaccinating the entire population within vaccine-producing countries first and then distributing vaccines to non-vaccine-producing countries. With limited doses among low-income countries, prioritizing the elderly who are at high risk of dying, together with the key front-line workforce who are at high risk of exposure is projected to be economically beneficial (e.g., 0.9%~3.4% annual GDP in India). Our results reveal how equitable distributions would cascade more protection of vaccines to people and ways to improve vaccine equity and accessibility globally through international collaboration.

China produced over half the world's coal-fired power capacity. Using a recent and comprehensive dataset of 1269 Chinese coal-fired power plants from 2009 to 2019, this paper provides empirical evidence of the impact of China's carbon trading pilot program on emissions regulation. Results show the significant potential for emis-sions intensity reduction due to increasing carbon prices, especially for low-risk, low-efficiency but high-cost plants, particularly those in China's midland and western regions. Rising marginal abatement costs and accel-erated depreciation from carbon prices may encourage utilities to retire high-emission power plants sooner than originally planned, leading to lower emissions intensity. The average retirement years of China's power plants can be shortened by 1.8001 and 1.6862 years under R2CUT and R2LUMP scenarios, respectively, as Cao et al. (2016). This study offers new insights into the impact of carbon prices on power plants and has important policy implications.

This study develops policy recommendations for the utilization of digital tools to enhance climate change adaptation in China, in response to the ever-transforming living environments due to climate change effects. As the digital economy in China continues to progress, leveraging these technologies to augment adaptation capabilities is deemed both essential and advantageous. This article first identifies China's primary climate change adaptation challenges, followed by an examination of successful digital solutions from countries outside of China. These solutions are then evaluated in the Chinese context, leading to the formation of policy recommendations to advance similar initiatives.

This article investigates the economic impacts of a multi-disaster mix comprising extreme weather, such as flooding, pandemic control, and export restrictions, dubbed a "perfect storm." We develop a compound-hazard impact model that improves on the ARIO model by considering the economic interplay between different types of hazardous events. The model considers simultaneously cross-regional substitution and production specialization, which can influence the resilience of the economy to multiple shocks. We build scenarios to investigate economic impacts when a flood and a pandemic lockdown collide and how these are affected by the timing, duration, and intensity/strictness of each shock. In addition, we examine how export restrictions during a pandemic impact the economic losses and recovery, especially when there is the specialization of production of key sectors. The results suggest that an immediate, stricter but shorter pandemic control policy would help to reduce the economic costs inflicted by a perfect storm, and regional or global cooperation is needed to address the spillover effects of such compound events, especially in the context of the risks from deglobalization.

The highly energy-intensive iron and steel industry contributed about 25% (ref. 1 ) of global industrial CO 2 emissions in 2019 and is therefore critical for climate-change mitigation. Despite discussions of decarbonization potentials at national and global levels 2 – 6 , plant-specific mitigation potentials and technologically driven pathways remain unclear, which cumulatively determines the progress of net-zero transition of the global iron and steel sector. Here we develop a CO 2 emissions inventory of 4,883 individual iron and steel plants along with their technical characteristics, including processing routes and operating details (status, age, operation-years etc.). We identify and match appropriate emission-removal or zero-emission technologies to specific possessing routes, or what we define thereafter as a techno-specific decarbonization road map for every plant. We find that 57% of global plants have 8–24 operational years, which is the retrofitting window for low-carbon technologies. Low-carbon retrofitting following the operational characteristics of plants is key for limiting warming to 2 °C, whereas advanced retrofitting may help limit warming to 1.5 °C. If each plant were retrofitted 5 years earlier than the planned retrofitting schedule, this could lead to cumulative global emissions reductions of 69.6 (±52%) gigatonnes (Gt) CO 2 from 2020 to 2050, almost double that of global CO 2 emissions in 2021. Our results provide a detailed picture of CO 2 emission patterns associated with production processing of iron and steel plants, illustrating the decarbonization pathway to the net-zero-emissions target with the efforts from each plant.

Pandemics such as COVID-19 and their induced lockdowns/travel restrictions have a significant impact on people’s lives, especially for lower-income groups who lack savings and rely heavily on mobility to fulfill their daily needs. Taking the COVID-19 pandemic as an example, this study analysed the risk of returning to poverty for low-income households in Hubei Province in China as a result of the COVID-19 lockdown. Employing a dataset including information on 78,931 government-identified poor households, three scenarios were analysed in an attempt to identify who is at high risk of returning to poverty, where they are located, and how the various risk factors influence their potential return to poverty. The results showed that the percentage of households at high risk of returning to poverty (falling below the poverty line) increased from 5.6% to 22% due to a 3-month lockdown. This vulnerable group tended to have a single source of income, shorter working hours, and more family members. Towns at high risk (more than 2% of households returning to poverty) doubled (from 27.3% to 46.9%) and were mainly located near railway stations; an average decrease of 10–50 km in the distance to the nearest railway station increased the risk from 1.8% to 9%. These findings, which were supported by the representativeness of the sample and a variety of robustness tests, provide new information for policymakers tasked with protecting vulnerable groups at high risk of returning to poverty and alleviating the significant socio-economic consequences of future pandemics.

Subnational and non-governmental actors are expected to provide important contributions to broader climate actions.A consistent and accurate quantification of their GHG emissions is an important prerequisite for the success of such efforts.However, emissions embodied in domestic and international supply chains, that can undermine the effectiveness of climate agreements, add challenges to the quantification of emissions originating from the consumption of goods and services produced elsewhere.We examine emission transfers between the states that have joined the U.S. Climate Alliance (USCA) and others.Our results show that states pledging to curb emissions consistent with the Paris Agreement were responsible for approximately 40% of total U.S. territorial GHG emissions.However, when accounting for transferred emissions through international and interstate supply chains of the products they consume, the share of Alliance states increased to 52.4% of the national total GHG emissions.The consumption-based emissions for some Alliance states, such as Massachusetts and New York, could be more than 1.5 times higher than their production-based emissions.Our detailed sectoral analysis highlights the challenges facing such agreements to extend cooperation in the future for larger joint benefit given the potential for carbon leakage from member states implementing stricter environmental policies that could lead to higher emissions from non-member states.It is critical for these arrangements to pay close attention to transferred emissions.

AbstractInternational efforts to avoid dangerous climate change have historically focused on reducing energy-related CO2 emissions from countries with either the largest economies (e.g. the EU and the USA) and/or the largest populations (e.g. China and India). However, in recent years, emissions have surged among a different and much less-examined group of countries, raising concerns that a next generation of high-emitting economies will obviate current mitigation targets. Here, we analyse the trends and drivers of emissions in each of the 59 countries where emissions in 2010–2018 grew faster than the global average (excluding China and India), project their emissions under a range of longer-term energy scenarios and estimate the costs of decarbonization pathways. Total emissions from these ‘emerging emitters’ reach as much as 7.5 GtCO2/year in the baseline 2.5° scenario—substantially greater than the emissions from these regions in previously published scenarios that would limit warming to 1.5°C or even 2°C. Such unanticipated emissions would in turn require non-emitting energy deployment from all sectors within these emerging emitters, and faster and deeper reductions in emissions from other countries to meet international climate goals. Moreover, the annual costs of keeping emissions at the low level are in many cases 0.2%–4.1% of countries’ gross domestic production, pointing to potential trade-offs with poverty-reduction goals and/or the need for economic support and low-carbon technology transfer from historically high-emitting countries. Our results thus highlight the critical importance of ramping up mitigation efforts in countries that to this point have been largely ignored.

In this article, we introduce two community-contributed data envelopment analysis commands for measuring technical efficiency and productivity change in Stata. Over the last decades, an important theoretical progression of data envelopment analysis, a nonparametric method widely used to assess the performance of decision-making units, is the incorporation of undesirable outputs. Models able to deal with undesirable outputs have been developed and applied in empirical studies for assessing the sustainability of decision-making units. These models are getting more and more attention from researchers and managers. The teddf command discussed in the present article allows users to measure technical efficiency, both radial and nonradial, when some outputs are undesirable. Technical efficiency measures are obtained by solving linear programming problems. The gtfpch command we also describe here provides tools for measuring productivity change, for example, the Malmquist–Luenberger index and the Luenberger indicator. We provide a brief overview of the nonparametric efficiency and productivity change measurement accounting for undesirable outputs, and we describe the syntax and options of the new commands. We also illustrate with examples how to perform the technical efficiency and productivity analysis with the newly introduced commands.

East Africa is typical of the less developed economies that have emerged since the 21st century, whose brilliant economic miracle has also triggered the rapid growth of energy consumption and carbon dioxide emissions. However, previous carbon accounting studies have never focused on the region. Based on multi-source data, this paper rebuilt the 45-sectors carbon emission inventories of eight East African countries from 2000 to 2017, and used index decomposition analysis to quantify the drivers of growth. Here we found that overall the CO2 emissions show a 'two-stage exponential growth' pattern, with significant heterogeneity between countries. In terms of the energy mix, technical progress in hydro and geothermal energy was almost offset by a growing appetite for oil and coal, making it the weak and valuable factor driving emissions reduction (-1.4Mt). But it was far from enough to overcome the pressure of economic and population growth, which brought about a 13Mt and 11Mt emission growth respectively from 2000 to 2017. Increasing energy intensity due to industrialization and transport development also contributed to an increment of 6.4Mt. Low-carbon policies should be tailored to local conditions and targeted at the improvement of energy efficiency and use of renewable energy so as to achieve a win-win situation between sustainable economic growth and emission reduction.

Cities are pivotal hubs of socioeconomic activities, and consumption in cities contributes to global environmental pressures. Compiling city-level multi-regional input-output (MRIO) tables is challenging due to the scarcity of city-level data. Here we propose an entropy-based framework to construct city-level MRIO tables. We demonstrate the new construction method and present an analysis of the carbon footprint of cities in China's Hebei province. A sensitivity analysis is conducted by introducing a weight reflecting the heterogeneity between city and province data, as an important source of uncertainty is the degree to which cities and provinces have an identical ratio of intermediate demand to total demand. We compare consumption-based emissions generated from the new MRIO to results of the MRIO based on individual city input-output tables. The findings reveal a large discrepancy in consumption-based emissions between the two MRIO tables but this is due to conflicting benchmark data used in the two tables.