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.

Calculating the proper social cost of carbon (SCC) is essential for effective climate policy. This paper argues for differentiated regional SCCs and a global SCC indicator based on the Lindahl equilibrium by treating climate change as an externality. The regional Lindahl SCC represents the regional "personalized prices" or "willing-to-pay" cost-sharing in global GHG mitigation efforts. It also acts as a consensus tool for international cooperation on carbon emissions. To demonstrate the feasibility and advantages of the Lindahl SCC, it is calculated using the RICE2020 model and compared with the regional SCC based on a utilitarian (equal-weight) social optimum. Numerical simulations support the findings. Finally, the policy implications of the Lindahl SCC are discussed.

This paper studies the impacts of regional breakdowns or model dimensionality on the model’s optimal solutions. Using the United States (USA) and China (CHN) as the experimental subject, we test the various solutions related to USA and CHN, such as the Cournot-Nash equilibrium and the Lindahl equilibrium, in the RICE2020 model under three regional breakdowns. Their solutions’ invariance and variances across different model dimensionalities indicate that modeling dimensionality may play a role in the strategic interactions among the regions in GHG mitigation. The simulation results also point out the pitfalls of the model comparisons across IAMs for climate change.

This study extends the classic RICE model by introducing energy factors into the economic module and comprehensively describes different types of energy demands. Taking China as an example, we constructed the RICE-China model and further explored the impact of different cooperation methods on China's carbon emissions and energy demand. The main results are as follows. First, there are significant differences in China's emission reduction under different cooperation scenarios. In the Lindahl cooperation scenario, China's carbon emissions in 2100 have reduced by 90.5 % to achieve the two-degree goal, which is lower than the utilitarian cooperation scenario. Second, the decline in China's fossil energy under the utilitarian scenario is higher than that under the Lindal scenario. Specifically, China's fossil energy demand decreased by 91.4 % in 2100 under the Lindal scenario, with non-fossil energy accounting for 94.7 % of total energy consumption. Third, China's emission reduction in the later period under the RICE-China model is lower than that of the RICE model, and the corresponding GDP loss has also decreased. Specifically, China's GDP losses under the RICE-China model are approximately 1.5-2.8% points lower than those under the RICE model. This study provides new insights for China to participate in international climate cooperation.

To achieve the carbon-neutral goal set for 2060, China has promoted renewable energy development for more than two decades. Meanwhile, China's electricity market is undergoing a new round of reform by introducing more market-oriented mechanisms and changing the market structure. This paper investigates the impact of three electric utility market structures experimented with in China on promoting renewable energy through a simulation model. The three market structures are vertical monopoly, weak monopoly (separation of power generators and grids), and monopolistic competition (Cournot-Nash competition). The model is calibrated with the actual data of the regional utility network in Beijing, Tianjin, and Tangshan areas. The simulation results show that the electricity market structure plays a vital role in renewable electricity, and the competition among utilities tends to benefit renewable energy development (wind power here). The study's conclusions are helpful to our understanding of further reforms in the electric utility industry and the long-term clean energy target in China.

As one of the soil types, peat is an important soil carbon storage and archive of past environmental changes. Here we used multi-core and multi-proxy records from a peatland near Da’erbin Lake in the Arxan region of Northeast China to reconstruct peatland development and carbon accumulation history and to understand their responses to past climate changes during the last 2500 years. Our macrofossil results show that the peatland was characterized by a sedge-dominated fen from 490 BCE to 1450 CE, changed to a Sphagnum-dominated poor fen or bog with abundant shrubs (mostly Ericaceae) during the period of 1450–1960 CE, and finally became predominated by Sphagnum after 1960 CE. The time-weighted mean apparent carbon accumulation rate (aCAR) from three cores range from 19.5 to 53.0 g C m-2 yr-1 with a mean value of 32.4 g C m-2 yr-1, but increase rapidly to 139.2 g C m-2 yr-1 during last several decades. During the early stage of the past 2500 years, three coring sites that are only 50 m apart were all in the fen phase but they had highly variable peat properties. The fen-bog transition occurred at different times at these sites due to local influences of autogenic process, permafrost dynamics, or fire disturbance. These observations suggest that fens are highly heterogeneous, not only in peat properties but also in ecosystem dynamics. The dramatic increase in aCAR during the late stage of bog phase after 1960 CE cannot be explained entirely by limited decomposition of recently-accumulated peat. Instead, this was likely due to increasing Sphagnum dominance and resultant low decomposition of Sphagnum-derived organic matter, suggesting the important role of vegetation change in controlling carbon accumulation rates. Around the 1990s CE, an increase in allogenic CAR—after removing the age-related long-term autogenic effect—seems to correspond with a period of increase in regional summer precipitation, revealing a sensitive response of ombrotrophic bog ecosystem to climate change at decadal timescale.

Abstract The Paris Agreement set up a blueprint for global GHG mitigation. Specifically, it specifies the temperature target by the end of the 21st century, outlines nationally determined contributions (NDCs), and periodical communication arrangements. In the literature on climate change economics, the above components of the Paris Agreement are not modeled simultaneously. In this paper, we adopt an overlapping generations (OLG) approach in the RICE2020 model to capture the above mechanisms of the Paris Agreement. Incorporating the OLG approach in an integrated assessment model (IAM) provides a rigorous economic interpretation of the mechanism set in the Paris Agreement. Through multi-scenario simulations in RICE2020, we conclude that the temperature target is not achievable under the NDCs and periodical communications mechanisms.

This chapter introduces the basic modelling framework of environmental externality. The modelling framework include four inter-connected models: SEEE, SEEN, DEEE, and DEEN models. They capture the externality feature of pollution in static or dynamic setting. We explain the mathematical and economic characteristics of these models in details. We also discuss the limitation and the issues beyond the basic models.

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.

Climate change is an externality phenomenon. The DICE/RICE models are IAMs that treat climate change as an externality explicitly. Such a feature of DICE/RICE is recognized by the Nobel Committee and is one of the primary reasons for its influence. This paper argues the essentiality of incorporating external effects of climate change in our understanding of climate change from a socio-economic perspective; points out the biases of missing the externality elements in climate change economics; outlining the crucial role of externality in IAM modeling.

Energy system is a crucial component of an economy. Measuring the performance and healthiness of energy system is very useful to decision makers. A well-designed energy index could serve such a purpose. Due to complexity of energy system in an economy, we have yet seen a widely accepted and comprehensive energy index in the literature. In this paper, we develop a comprehensive multidimensional energy index (CMEI) based on the harmonic average of multiple sub-indices. We show that CMEI possesses good analytical properties. Using the data from the United States and China, we demonstrate the applicability of CMEI and test some of its desirable properties. Our conclusion is that CMEI can be used in time-series and cross-regional diagnosis and comparison of energy systems and serves as a simple and handy tool for policy makers.

Increasing returns to scale (IRS) phenomena are widely present in economies, and integrated assessment models (IAMs) for climate change generally assume constant returns to scale (CRS). This paper studies the connection between IRS in energy-intensive sectors and the regions' attitude towards climate change. In a model of detrimental (negative) externality, we proved that if some agents' activities related to externality generation exhibit IRS, their optimal target in the efficient externality provision can be very close to their inefficient non-cooperative Cournot-Nash equilibrium position. The numerical simulations in this paper confirm the analytical conclusion. The analytical results point out the potential biases of IAMs under the CRS assumption and provide a better explanation of difficulties in international climate negotiations. Finally, the paper offers some policy suggestions on climate negotiation in the presence of IRS.

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.