Emerging countries are the main source of new CO₂ emissions and the major net carbon importers, and they have also become an important part of the global trade pattern. In this study, the impact of trade openness on CO₂ emissions was investigated by approaches such as fully modified least squares (FMOLS), dynamic ordinary least squares (DOLS), and pooled mean group-autoregressive distributive lag (PMG-ARDL) methods. Further estimations were conducted by employing methods such as DCCEMG (dynamic common-correlated effect mean group) and Driscoll–Kray to strengthen the robustness of the results. Moreover, the Granger causality between trade openness and CO₂ emissions was tested by using the Dumitrescu–Hurlin method. Conclusions can be drawn as follows: First, economic growth, energy consumption, trade openness, and CO₂ emissions are all interconnected in the long term. Specifically, higher levels of economic growth and trade openness are associated with lower CO₂ emissions, whereas energy consumption contributes to higher emissions. However, in the short term, economic growth and energy consumption lead to an increase in CO₂ emissions, while trade openness does not have a significant impact. Moreover, there is a two-way Granger causality between trade openness and CO₂ emissions. Additionally, economic growth and energy consumption have an indirect effect on CO₂ emissions by influencing trade openness. Given these findings, emerging market countries should focus on enhancing their service sectors, promoting technological advancements, and fostering international collaboration in green technologies. By actively engaging in efforts to combat climate change, these countries reach a point where trade expansion and carbon reduction are achieved.

With carbon emissions continuing to rise, global warming has become a popular research topic. To address climate change, China is taking proactive measures to reduce its carbon emissions. Covering the period between 2009 and 2021, this study utilizes data from 3668 publicly listed companies in China, along with data from their respective cities, to investigate the impact of the carbon emissions trading pilot policy on their environmental, social, and governance (ESG) performance. The conclusions show that the policy has greatly improved corporate ESG performance. However, its impact on the corporate ESG performance has varied over time, across different entities, and among different cities. Furthermore, the companies’ level of green innovation plays a crucial intermediary role. Additionally, a company’s risk-bearing capacity and level of urban green credit support positively moderate the effectiveness of the policy. These findings enrich our understanding of the relationship between the pilot policy and corporate ESG performance.

The Yangtze River Economic Belt plays a crucial role in the economic development of China from the perspective of economic growth, as well as from the perspective of the goal of the emission peak and carbon neutrality. In this study, the dynamic and causal relationships among economic growth, energy consumption and CO₂ emissions of the provinces and municipalities along the Yangtze River Economic Belt were analyzed using the panel vector autoregression (PVAR) model. The results revealed that the economic growth and the changes in CO₂ emissions in the current period are significantly influenced by the economic growth and CO₂ emissions in the preceding period, while the energy consumptionin the current period is influenced by the economic growth, energy consumption and CO₂ emissions in the preceding period. The results of the Granger causality test based on panel data suggested that among the three sets of causalities involving economic growth, energy consumption and CO₂ emissions, only economic growth and CO₂ emissions have one-way causality, while the other two sets of causalities are two-way. Further, the results of the impulse response function and variance decomposition showed significantly positive effects and a certain degree of path dependence among economic growth, energy consumption and CO₂ emissions.

The spatial-temporal mismatch of water resources and socio-economic development in rapidly urbanized regions has been the focus of water resource management, and is one of the main limitations to sustainable development goals (SDGs). Guangdong Province is represented by a highly developed economy and society, and has been undergoing disproportionately rapid population growth during the past two decades. The uneven distribution and spatial mismatch of water resources have aggravated the contradictions between water supply demand. In this paper, we evaluate the matching degree of water resources and social economic elements, detect the spatial-temporal changing pattern of the matching degree, and reveal the changing mechanism using the combined methods of the Thiel index, the space–time Gini coefficient, and the Lorenz curve. The results show that (1) the temporal matching is relatively balanced and reasonable, while the spatial mismatch is prominent and deteriorating constantly, especially the connection between the amount of water and GDP; (2) the water volume pressure is mainly concentrated in the Peral River Delta and eastern and western Guangdong, while water consumption stress is relatively lower in northeastern Guangdong; and (3) the water volume inequality is dominated by an intercity difference and is primarily caused by regional differences. Based on the results, policy implications, such as the implementation of integrated water resource management plans, investment in the development of alternative water sources, as well as enhanced public education and the establishment of a water saving society, have been provided to alleviate the mismatch issue between water elements and socio-economic parameters, and to push the realization of water-related SDGs.

Newly Industrialized Countries (NICs) represent the most dynamic economic clusters globally and drive the marginal growth of global carbon emissions, rendering their carbon mitigation performance pivotal to delivering the climate targets pledged under the Paris Agreement. Grounded in the Environmental Kuznets Curve (EKC) hypothesis, this study applies the cross-sectional augmented autoregressive distributed lag (CS-ARDL) framework to the panel data from NICs for the 1990–2022 period and incorporates dynamic autoregressive distributed lag simulation methods to investigate the dynamic relationship between energy transition and CO₂ emissions across these economies. The CS-ARDL estimation results suggest that the conventional inverted U-shaped Environmental Kuznets Curve (EKC) hypothesis is invalid under the conditions of cross-sectional dependence and slope heterogeneity for NICs. Per capita GDP growth is found to exert a significant negative impact on CO₂ emissions in these economies. In both the long and short run, rising per capita non-renewable energy consumption substantially increases CO₂ emissions, whereas a higher share of renewable energy in total final energy consumption yields a significant CO₂ mitigation effect. Estimations conducted via the Augmented Mean Group (AMG), Common Correlation Effect Mean Group (CCEMG), and Dynamic Common Correlation Effect Mean Group (DCCEMG) estimators yield consistent findings with the CS-ARDL model. Dynamic ARDL simulation results reveal that positive shocks to per capita non-renewable energy consumption and renewable energy share in total final energy consumption induce heterogeneous dynamic response trajectories of CO₂ emissions across countries. Nevertheless, rising per capita non-renewable energy consumption triggers an overall sustained growth in CO₂ emissions, while a higher proportion of renewable energy achieves long-term mitigation of CO₂ emissions.