Developing an evidence-based subnational climate vulnerability index is essential for prioritizing regional adaptation support and ensuring the fair and effective allocation of related resources. However, existing indices often struggle to capture nonlinear links between climatic drivers and economic outcomes, and their largely static perspective overlooks how future uncertainties-arising from mitigation pathways, projected warming, and macroeconomic responses-can reshape both climate risks and regional capacity. To address these gaps, we develop a probabilistic climate-economy risk-assessment framework that propagates these nonlinearities and uncertainties into province-level vulnerability distributions, and use them to construct an integrated vulnerability index that reflects both the central tendency and tail risk of temperature-induced GDP vulnerability. Applied to China, approximately 64.5%-80.6% of provinces are classified as vulnerable, with index values below zero. The index reveals a clear spatial gradient, with vulnerability higher in the east and south: coastal economic hubs such as the Yangtze River Delta and Pearl River Delta, together with tropical Hainan, are most vulnerable, whereas colder northwestern and northeastern provinces are more likely to be resilient. The framework supports flexible adaptation resource allocation and regionally targeted investment prioritization under evolving mitigation pathways.

PurposeAgricultural production significantly emits greenhouse gases and pollutants, degrading environmental systems that, in turn, impact crop yields. Achieving climate neutrality and Sustainable Development Goals necessitates balancing climate action, pollution control and sustainable agriculture. This requires a systematic understanding of the links between environmental factors and crop yields, particularly the impact mechanisms of agriculture–environment interactions and advances in integrated climate–biophysical–economic modeling. However, existing research remains fragmented across various disciplines and domains. This paper aims to provide a review and conclude in this field. Design/methodology/approachThis paper first identifies the key research boundaries on environmental factors and modeling tools through bibliometric analysis. It then summarizes the interaction mechanism between environmental systems and crop yields, and further systematically reviews the advantages, disadvantages and applicability of different modeling approaches in this field. FindingsThis review synthesizes and examines the key biophysical mechanisms linking environmental factors to crop yields, focusing on four major drivers: climate change, ozone pollution, nitrogen fertilizer use and soil organic carbon. It identifies critical modeling challenges, including scale mismatches between biophysical and economic models, spatial resolution limitations in ozone effects, underrepresentation of nitrogen’s economic costs and long-term dynamics of soil organic carbon. Research limitations/implicationsAddressing these gaps requires integrating process-based and statistical models, multisource data calibration, constructing marginal cost curves and leveraging remote sensing and geospatial technologies. These advances are essential for developing policies that align agricultural productivity with environmental sustainability. Originality/valueThis paper synthesizes key biophysical mechanisms and explores the development trajectories and characteristics of modeling tools, identifying the associated strengths, practical applicability, key limitations and methodological challenges. It also proposes directions for future research to enhance analytical frameworks.

Climate econometric analysis of the relationship between temperature and gross domestic product (GDP) is increasingly being used to evaluate climate risks and understand economic impacts caused by climate change. We review the literature on growth and level effects (i.e., temperature rise respectively affects the growth and level of economic output), the setting of temperature variables' forms and functional forms, and the inherent model specification of climate econometrics. Additionally, we introduce an approach for combining empirical findings with climate change integrated assessment models (IAMs) to improve damage modelling. Our findings show that estimates of damage through growth effects are generally much larger than those through level effects. Diverse impact mechanisms and adaptation effects can be revealed by changing the time resolution of temperature variables, introducing non-linearity into econometrics functions, and specifying temperature deviation. Combing the cross-sectional and panel model would enable us to examine the economic impacts at different future times.

A strategy that informs on countries’ potential losses due to lack of climate action may facilitate global climate governance. Here, we quantify a distribution of mitigation effort whereby each country is economically better off than under current climate pledges. This effort-sharing optimizing approach applied to a 1.5 °C and 2 °C global warming threshold suggests self-preservation emissions trajectories to inform NDCs enhancement and long-term strategies. Results show that following the current emissions reduction efforts, the whole world would experience a washout of benefit, amounting to almost 126.68–616.12 trillion dollars until 2100 compared to 1.5 °C or well below 2 °C commensurate action. If countries are even unable to implement their current NDCs, the whole world would lose more benefit, almost 149.78–791.98 trillion dollars until 2100. On the contrary, all countries will be able to have a significant positive cumulative net income before 2100 if they follow the self-preservation strategy.

China, the second largest economy in the world, covers a large area spanning multiple climate zones, with varying economic conditions across regions. Given this variety in climate and economic conditions, global warming is expected to have heterogeneous economic impacts across the country. This study uses annual average temperature to conduct an empirical research from a top-down perspective to evaluate the nonlinear impacts of temperature change on aggregate economic output in China. We find that there is an inverted U-shaped relationship between temperature and economic growth at the provincial level, with a turning point at 12.2°C. The regional and national economic impacts are projected under the shared socioeconomic pathways (SSPs) and representative concentration pathways (RCPs). As future temperature rises, the economic impacts are positive in the northeast, north, and northwest regions but negative in the south, east, central, and southwest regions. Based on SSP5, the decrement in the GDP per capita of China would reach 16.0% under RCP2.6 and 27.0% under RCP8.5.

Because free-riding behavior is an inherent characteristic of climate change, how to protect the economic benefits of the emission reduction regions and prompt the noncooperative region to join the emission reduction coalition is particularly important. In this study, we use a global multi-region multi-sector CGE model to compare the impacts of border carbon adjustment (BCA) and two unified tariff mechanisms based on different implementation principles on USA. The results show that the BCA is more effective in reducing carbon leakage in USA than the uniform tariff mechanisms. However, for GDP and welfare losses, the scenario Tariff-carbon-reduction results in greater GDP and welfare losses in USA, which is more conducive to prompting USA to implement carbon reduction policies than the BCA measures. Finally, the sensitivity analysis of carbon price levels and key substitution elasticity further confirmed the results.

A series of global actions have been made to address climate change. As a recent developed climate policy, Intended Nationally Determined Contributions (INDC) have renewed attention to the importance of exploring temperature rise levels lower than 2 °C, in particular a long-term limit of 1.5 °C, compared to the preindustrial level. Nonetheless, achieving the 2 °C target under the current INDCs depends on dynamic socioeconomic development pathways. Therefore, this study conducts an integrated assessment of INDCs by taking into account different Shared Socioeconomic Pathways (SSPs). To that end, the CEEP-BIT research community develops the China’s Climate Change Integrated Assessment Model (C 3 IAM) to assess the climate change under SSPs in the context of with and without INDCs. Three SSPs, including “a green growth strategy” (SSP1), “a more middle-of-the-road development pattern” (SSP2) and “further fragmentation between regions” (SSP3) form the focus of this study. Results show that after considering INDCs, mitigation costs become very low and they have no evident positive changes in three SSPs. In 2100, a temperature rise would occur in SSP1-3, which is 3.20, 3.48 and 3.59 °C, respectively. There are long-term difficulties to keep warming well below 2 °C and pursue efforts toward 1.5 °C target even under INDCs. A drastic reduction in greenhouse gas emissions is needed in order to mitigate potentially catastrophic climate change impacts. This work contributes on realizing the hard link between the earth and socioeconomic systems, as well as extending the economic models by coupling the global CGE model with the economic optimum growth model. In C 3 IAM, China’s energy consumption and emissions pattern are investigated and refined. This study can provide policy makers and the public a better understanding about pathways through which different scenarios could unfold toward 2100, highlights the real mitigation and adaption challenges faced by climate change and can lead to formulating effective policies.

Yi-Ming Wei a,b,c, , Rong Han, Qiao-Mei Liang, Bi-Ying Yu, Yun-Fei Yao, Mei-Mei 4 Xue , Kun Zhang , Li-Jing Liu , Juan Peng , Pu Yang, Zhi-Fu Mi, Yun-Fei Du 5 , Ce Wang, Jun-Jie Chang, Qian-Ru Yang, Zili Yang,Xueli Shi, Wei Xie, Changyu 6 Liu, Zhongyu Ma, Jinxiao Tan,Weizheng Wang, Bao-Jun Tang, Yun-Fei Cao , 7 Mingquan Wang, Jin-Wei Wang, Jia-Ning Kang, Ke Wang, Hua Liao * 8 9 10 Center for Energy and Environmental Policy Research, Beijing Institute of Technology, 11 Beijing 100081, China 12 School of Management and Economics, Beijing Institute of Technology,Beijing 100081, 13 China 14 Beijing Key Lab of Energy Economics and Environmental Management, Beijing 100081, 15 China 16 The Bartlett School of Construction and Project Management, University College London, 17 WC1E 7HB, London, UK 18 Department of Economics, State University of New York at Binghamton, Binghamton, NY 19 13902, USA 20 f The National Climate Center of China Meteorological Administration, Beijing100081, China 21 g School of Advanced Agriculture Sciences, Peking University, Beijing 100871, China 22 The National Information Center of National Development and Reform Commission, 23 Beijing100045, China 24 Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 25 201210,China 26 27 28 29 30 31 32 Abstract: 33