Pathways for regions to achieve carbon emission peak: New insights from the four economic growth poles in China

Synergistic allocation of carbon emission and energy quotas: A balancing act under carbon peaking constraints

Carbon emissions quotas (CEQ) and energy quotas (EQ) have been recognized as effective policy tools for promoting energy conservation and emission reduction. However, existing methods have considerable limitations in meeting the phased requirements of carbon peaking constraints and synergistically allocating EQ and CEQ. Therefore, we propose a synergistic allocation method for CEQ and EQ to balance economic development and environmental sustainability under carbon peak constraints. Using 30 provinces in China as case studies, a dual-layer allocation model was constructed by integrating an indicator method with an improved ZSG-DEA model to achieve the coordinated management and target control of CEQ and EQ. Reaching energy and carbon emission targets under peak constraints, this approach avoids excessive reductions in regional quota usage rights, and promotes equitable and efficient interprovincial quota distribution. A high correlation was noted between CEQ and EQ among the provinces, highlighting the necessity for synergistic quota management. Energy-intensive regions and cleaner western regions require opposite quotaadjustment strategies, resulting in quota surpluses and deficits across the provinces. Reductions in energy-intensive provinces such as Hebei, Inner Mongolia, Shanxi, and Xinjiang should be maintained within 20%, whereas cleaner western provinces are expected to achieve quota surpluses. This differentiated adjustment strategy helps to narrow the interprovincial quota gap and promotes regional coordination. Under peak carbon targets, economic growth requires the combined efforts of a quota allocation mechanism, efficient use strategies, and economic structural transformation. These findings can inform multiscale environmental resource quota allocation, contributing to carbon peaks and development goals.

Research on the prediction and realization path of urban carbon peak along the Yellow River Basin

China and the international community attach great importance to sustainable development goals such as peaking carbon emissions. As an important energy base in China, the Yellow River Basin has significant capacity for reducing carbon emissions and increasing carbon sinks. To accelerate the achievement of carbon peak targets, social and natural development data as well as carbon emission data of 56 cities along the Yellow River Basin from 2000 to 2021 were analyzed. Based on the evaluation indicators of the model, the PSO-XGBoost-RF model was selected from the comparative models to predict the carbon peak of cities in the Yellow River Basin under different paths. Under the current economic and social development, cities along the Yellow River Basin in China are facing huge pressure to reduce emissions. It is expected to achieve a carbon peak in 2033, with a peak of 2,051,320,000 tons, so the target of reaching the peak of carbon emissions before 2030 will not be accomplished. But through comprehensive optimization of the industrial structure and reduction of energy consumption, cities along the Yellow River Basin as a whole may achieve carbon peak by 2026, with a peak of 1,917,132,000 tons.

Spatial spillover effect and driving factors of urban carbon emissions in the Yellow River Basin using nighttime light data

Yellow River Basin (YRB) is a pivotal region for energy consumption and carbon emissions (CEs) in China, with cities emerging as the main sources of regional CEs. This highlights their critical role in achieving regional sustainable development and China's carbon neutrality. Consequently, there is a pressing need for a detailed exploration of the urban spillover effects and an in-depth analysis of the complex determinants influencing CEs within the YRB. Remote sensing data provide optimal conditions for conducting extensive studies across large geographical areas and extended time periods. This study integrates DMSP/OLS and NPP/VIIRS nighttime light datasets for a longitudinal analysis of urban CEs in the YRB. Using a harmonized dataset from DMSP/OLS and NPP/VIIRS nighttime light from 2007 to 2021, this study quantifies CEs of 58 prefecture-level cities in the YRB. By combining ESDA, STIRPAT model and spatial econometric model, this investigation further clarifies empirically the spatial spillover effects and driving factors of urban CEs. The analysis delineates a phase-wise augmentation in urban CEs, converging towards a distinct spatial distribution characterized by "lower reach > middle reach > upper reach". The spatial autocorrelation tests unravel a complex interplay between agglomeration and differentiation patterns within urban CEs, underscored by pronounced spatial lock-in phenomena. Significantly, this study demonstrates that urbanization, economic development, energy consumption structure, green coverage rate, industrial structure, population, technological progress, and FDI each exhibit varied direct and indirect effect on urban CEs. Furthermore, it elaborates on potential policy implications and future research directions, offering crucial insights for formulating CEs mitigation strategies to advance sustainable development.

Investigating the unparalleled effects of economic growth and high-quality economic development on energy insecurity in China: A provincial perspective

China has changed its focus from traditional high-speed economic growth to high-quality economic development (HQED) and the implementation of environmentally friendly practices. This transition can have parallel or unparallel impacts on energy insecurity (EIS). In this regards, HQED, inter Alia, is crucial in mitigating EIS and combating the energy crisis. Our study explores the impact of economic growth (EG) and HQED on EIS using the provincial panel data of China for the period 2011-2017. From the perspective of comparative analysis, the results reveal that HQED reduces EIS while EG increases it. The robustness checks indicate that industrial structure (IS) has a negative impact on EIS, whereas industrial structure upgrading (ISU) and green innovation (GI) have a positive influence. This implies that IS contributes to an increase in EIS, whereas ISU and GI result in a decrease in EIS. In addition, the analysis reveals that digital financial inclusion (DFI) exhibits a significant positive relation with EIS, albeit occasionally a negative but insignificant link. The policy implication is that the government should stimulate policies to promote HQED which reduces the EIS.