Weiwei Xiong

Whereas there is growing interest in exploring very long-term climate with overshoot beyond 2100, most integrated assessment models (IAMs) generate emissions scenarios only through 2100. Here we propose approaches for extending scenarios until 2150 using an emulator of IAMs. Our approaches offer a potential interim solution for developing very long-term scenarios, such as the scenario model intercomparison project, circumventing the challenges of fully simulating IAMs beyond 2100.

Integrating physical, socio-economic, and technological perspectives is indispensable for addressing climate mitigation challenges. While directly coupling state-of-the-art Earth System Models (ESMs) and Integrated Assessment Models (IAMs) offers a way to explore feedbacks between these domains, doing so with full-complexity models remains computationally prohibitive. This is particularly true for cost-effective intertemporal optimization IAMs due to fundamental operational differences: while ESMs perform forward simulations, such IAMs optimize over time. Consequently, direct coupling would require numerous computationally intensive iterations to converge, a complication further compounded by the stochastic nature of ESMs.To overcome the barriers to coupling ESMs and IAMs, we employ their reduced-complexity representations (i.e., emulators). We couple an IAM emulator representing 9 distinct IAMs (Xiong et al. 2025) with an ESM emulator, FaIR, representing 66 ESM configurations (Smith et al. 2024a). Using this coupled ESM-IAM emulator framework in an optimization setting, we calculate cost-effective pathways that achieve the temperature targets of the Paris Agreement with and without overshoot.Our preliminary results indicate that the uncertainty ranges for such pathways are significantly larger than previously estimated. Our results also have implications for target setting; we show how pathways differ when IAMs optimize directly for a temperature target – a capability IAMs traditionally lack. Instead, IAMs typically rely on temperature proxies, such as carbon budgets (or their corresponding carbon price pathways), which do not necessarily provide an accurate representation of the temperature target. Furthermore, this study offers advanced insights into the dynamics of climate-economy interactions, providing a roadmap for future efforts to couple full-complexity models. ReferencesXiong, W., Tanaka, K., Ciais, P., Johansson, D. J. A., & Lehtveer, M. (2025). emIAM v1.0: an emulator for integrated assessment models using marginal abatement cost curves. Geosci. Model Dev., 18(5), 1575-1612. doi:10.5194/gmd-18-1575-2025Smith, C., Cummins, D. P., Fredriksen, H. B., Nicholls, Z., Meinshausen, M., Allen, M., . . . Partanen, A. I. (2024). fair-calibrate v1.4.1: calibration, constraining, and validation of the FaIR simple climate model for reliable future climate projections. Geosci. Model Dev., 17(23), 8569-8592. doi:10.5194/gmd-17-8569-2024

Integrated Assessment Models (IAMs) combine economy, energy, and sometimes land-use modeling approaches and are commonly used to evaluate climate policies under least-cost scenarios. The marginal abatement cost (MAC) curve approach has been commonly used in climate policy analyses to show the carbon price level for a given abatement level, which has also been applied as a way to parameterize the complex behavior of IAMs. Here, we propose a new methodological framework to i) emulate the IAM’s emission reductions in response to carbon price pathways through MAC curves (i.e., IAM emulator) and then ii) extend IAM’s emission pathways (usually given until 2100) to 2300 with the emulator.As part of the Horizon Europe RESCUE and OptimESM projects, our approach is used to extend the greenhouse gas (GHG) emission pathways from different sources and carbon dioxide removal (CDR) pathways generated by the REMIND-MAgPIE model. A key feature of the approach is that we individually capture the emission reductions associated with CDRs (i.e., afforestation, bioenergy and carbon capture and storage (BECCS), direct air capture with carbon storage (DACCS), industrial CCS, and ocean alkalinity enhancement (OAE)) through MAC curves. Our approach relies on the following simplifying assumptions: i) MAC curves are assumed time-independent over periods, ii) abatement levels are assumed independent across GHGs (CO2, CH4, and N2O), sectors (energy- and non-energy-related emissions), and CDR options, and iii) a uniform carbon price is used across sectors and CDRs, with the GWP100 metric used to fix the price ratios between different GHGs.We approximated the dynamics of REMIND-MAgPIE with MAC curves, using equation log(f(x)+1)=a*xb+c*xd for sectoral gases and equation f(x)=a*xb+c*xd for CDR options, respectively. f(x) represents the corresponding carbon price level at x, while the variable x represents the abatement level relative to the assumed baseline level, expressed as a percentage for sectoral emissions or the absolute amount of CO2 removed for CDR options. a, b, c, and d are the parameters that are optimized for each case. Additionally, we derived the maximum abatement levels of REMIND-MAgPIE from its simulation results under all carbon budgets, which reflect the limit of, for example, CCS capacity and sectoral mitigation potential. We also calculated for each gas, sector, and CDR option the maximum first and second derivatives of temporal changes in abatement levels to capture the limits of the technological change rate and the socio-economic inertia.By combining the IAM emulator with a reduced-complexity climate model ACC2, we further derived extended emission pathways beyond 2100 using the least-cost approach for temperature trajectories (1 °C, 1.5 °C, and 2 °C) with overshoots of up to 2 °C. These pathways illustrate various CDR use cases over the coming centuries. Our extended scenarios, generated on the basis of long-term climate-economy interactions, can serve as input to Earth System Models investigating the long-term consequences of climate change mitigation strategies, particularly the implications of CDR deployment and associated Earth system dynamics over centennial timescales.

We developed an emulator for integrated assessment models (emIAM) based on a marginal abatement cost (MAC) curve approach. Drawing on the output of IAMs in the Exploring National and Global Actions to reduce Greenhouse gas Emissions (ENGAGE) Scenario Explorer and the GET model, we derived an extensive array of MAC curves, encompassing 10 IAMs, at the global and regional levels for 10 regions; three gases (CO2, CH4, and N2O); eight portfolios of available mitigation technologies; and two emission sources. We tested the performance of emIAM by coupling it with the simple climate model ACC2 (ACC2–emIAM). Our analysis showed that the optimizing climate–economy model ACC2–emIAM adequately reproduced a majority of the original IAM emission outcomes under similar conditions. This can facilitate systematic exploration of IAMs with small computational resources. emIAM holds the potential to enhance the capabilities of simple climate models as a tool for calculating cost-effective pathways directly aligned with temperature targets.

As an important means of regulating pollution emissions, environmental regulation is crucial for reducing urban PM2.5. However, previous studies have mainly focused on the emission reduction effect of formal environmental regulations and neglected the role played by informal environmental regulations represented by environmental information disclosure. We employed a multiperiod difference-in-differences (DID) model to assess the effectiveness of EID policies in mitigating PM2.5 emissions and to investigate their abatement mechanism by focusing on green innovation and industrial structure. The findings indicate that the implementation of EID policies significantly reduces PM2.5 emissions. Mechanism tests reveal that EID promotes PM2.5 reductions by fostering green innovation and upgrading industrial structure. In addition, the impact of EID policy is more marked in resource-based cities and those located in interior regions. This study contributes to the reduction in urban haze emissions in China, offering empirical evidence and policy recommendations for the further implementation of environmental information disclosure.

Most recent cost-benefit Integrated Assessment Models have used only one reference scenario and focused on reducing mainly CO₂ emissions. This goal may not adequately account for the uncertainties arising from diverse socioeconomic developments and the potential for mitigating the effects of emissions of individual greenhouse gases, aerosols, and pollutants. We developed an Integrated Assessment Model framework by combining a socioeconomic module with a reduced-complexity climate module. We represented the Shared Socioeconomic Pathways (SSP) scenarios by 1) calculating a new set of marginal abatement cost curves based on the most recent integrated assessment model, 2) creating a new SSP-dependent damage function based on process-based impact simulation results, and 3) extending the evaluation time to the year 2450. The cost-benefit analysis revealed that the SSP scenarios achieved various rates of control for emissions of individual greenhouse gases, aerosols, and pollutants. The result was diverse patterns of optimal temperatures, including maximum temperature achieved and stabilized temperature by the end of the evaluation period. The model simulations showed the importance of distinguishing options for reducing emissions of greenhouse gases based on distinct socioeconomic growth scenarios. We also show an example of a long-term socioeconomic projection spanning several centuries as well as a variety of socioeconomic assumptions for assessing climate change policies with long-term consequences.

We developed an emulator for Integrated Assessment Models (emIAM) based on a marginal abatement cost (MAC) curve approach. Using the output of IAMs in the ENGAGE Scenario Explorer and the GET model, we derived a large set of MAC curves: ten IAMs; global and eleven regions; three gases CO₂, CH₄, and N₂O; eight portfolios of available mitigation technologies; and two emission sources. We tested the performance of emIAM by coupling it with a simple climate model ACC2. We found that the optimizing climate-economy model emIAM-ACC2 adequately reproduced a majority of original IAM emission outcomes under similar conditions, allowing systematic explorations of IAMs with small computational resources. emIAM can expand the capability of simple climate models as a tool to calculate cost-effective pathways linked directly to a temperature target.

This paper expands research on the relationship marketing model of “customer gratitude→repeated purchase intention” by focusing on customers’ psychological and introducing customer psychological ownership, brand attachment, and product attitude as three mediator variables into the SOR theory. The PLS-SEM method is applied to analyze 283 questionnaires. The results are as follows. (1) Customer gratitude is an essential factor for customer psychological ownership, which has a significant positive impact on brand attachment and product attitude. (2) Brand attachment and product attitude are critical factors for consumers’ repeated purchase intention. (3) The mediating effects of customer psychological ownership, brand attachment, and product attitude were further confirmed. This paper can provide references for companies and service personnel on optimizing services and maximizing profits during competition.

Now that many countries have set goals for reaching net zero emissions by the middle of the century, it is important to clarify the role of each country in achieving the 1.5 °C target of the Paris Agreement. Here, we evaluated China’s role by calculating the global temperature impacts caused by China’s emission pathways available in global emissions scenarios toward the 1.5 °C target. Our results show that China’s contribution to global warming in 2050 (since 2005) is 0.17 °C on average, with a range of 0.1 °C to 0.22 °C. The peak contributions of China vary from 0.1 °C to 0.23 °C, with the years reached distributing between 2036 and 2065. The large difference in peak temperatures arises from the differences in emission pathways of carbon dioxide (CO₂), methane (CH₄), and sulfur dioxide (SO₂). We further analyzed the effect of the different mix of CO₂ and CH₄ mitigation trajectories in China’s pathways on the global mean temperature. We found that China’s near-term CH₄ mitigation reduces the peak temperature in the middle of the century, whereas it plays a less important role in determining the end-of-the-century temperature. Early CH₄ mitigation action in China is an effective way to shave the peak temperature, further contributing to reducing the temperature overshoot along the way toward the 1.5 °C target. This underscores the necessity for early CO₂ mitigation to ultimately achieve the long-term temperature goal.