Regional policy options for carbon peaking in the Yangtze River Delta under uncertainty.

The Yangtze River Delta (YRD) region plays a crucial role in achieving China's carbon peaking goal. However, due to uncertainties surrounding future economic growth, energy consumption, energy structure, and population, the attainment of carbon peaking in this region remains uncertain. To address this issue, this study utilized the generalized Divisia index method to analyze the driving factors of carbon emissions, including economy, energy, investment, and population. Subsequently, Monte Carlo simulations were combined with scenario analysis to dynamically explore the peak path of regional heterogeneity in the YRD from 2022 to 2035 under uncertain conditions. The findings highlighted that economic uncertainty has the most significant impact on carbon emissions. Furthermore, reducing energy intensity and promoting the transformation of the energy consumption structure contribute to carbon reduction. The study also revealed that the carbon peak in the YRD exhibits regional heterogeneity. According to the baseline scenario, carbon emissions in the YRD will not peak before 2035. However, under the low-carbon development scenario, the carbon emissions of Zhejiang and Shanghai will peak before 2030. Moreover, under the enhanced emission reduction (EE) scenario, carbon emissions in Jiangsu, Zhejiang, and Shanghai will peak before 2025, while Anhui will reach its peak before 2030. Collectively, the entire YRD region is forecasted to attain a carbon emissions peak of 2.29 billion tons by 2025 under the EE scenario. This study provides valuable insights into the carbon emission trajectories of the YRD region under uncertain conditions. The findings can be instrumental in formulating carbon peaking policies that account for regional heterogeneity.

Towards energy conservation by constructing more transportation infrastructure?: An endogenous stochastic frontier analysis framework.

Whether constructing more transportation infrastructure can be helpful for the achievement of energy conservation is a long-running and debatable issue. To answer this question, the relationship between transportation infrastructure and energy efficiency must first be clarified. Nonetheless, the existence of the endogeneity problem poses a challenge to defining the relationship. In this paper, an endogenous stochastic frontier analysis method is used to investigate the influence of transportation infrastructure on energy efficiency. Based on the prefecture-city level panel data in China, we find that after addressing the endogeneity problem, the impact of transportation infrastructure on energy efficiency increases dramatically. Moreover, this impact is more pronounced in small-scale cities compared to large and medium-scale cities. Regardless of the measurement of transportation infrastructure, instrumental variable, or production function form, we get the similar conclusions, demonstrating the robustness of our findings. Additional simulation analysis shows that the energy conservation potential would be 1222-2935 million kilowatt hours if the level of transportation infrastructure could be optimized. We recommend accelerating the transportation infrastructure construction, particularly in the small-scale cities so as to boost the energy efficiency and achieve energy conservation targets.

Exploring the role of green innovation and investment in energy for environmental quality: An empirical appraisal from provincial data of China.

To better analyze China's carbon neutrality target, this study investigates the effect of green innovation and investment in the energy industry on China's provincial and regional data from 1995 to 2017. Using Westerlund and Edgerton's panel cointegration test, the authors found a stable long-run relationship between CO2 emissions and its determinants. We found that under major structural breaks at the local, regional, and global levels, such as the East Asian crises of 1997, the financial crises of 2007-2008, China's RMB exchange rate reform announced on August 11, 2015, and mild recession in 2001, CO2 emissions, income, green innovation, renewable energy use, and energy industry investment are cointegrated. The environmental Kuznets curve hypothesis is valid. In the long run, income, environmental innovation, investment in the energy industry, and renewable energy consumption are key contributors in explaining CO2 emissions. The empirical evidence from augmented mean group (AMG) is consistent with the estimates of CS-ARDL. Concerning practical implications, the findings suggest that there is a need to switch the Chinese economy to more sustainable sources of energy, a viable solution to abate environmental degradation. China should introduce and shift investments to green innovation.

Forecasting carbon emissions future prices using the machine learning methods.

Due to the uncertainty surrounding the coupling and decoupling of natural gas, oil, and energy commodity futures prices, the current study seeks to investigate the interactions between energy commodity futures, oil price futures, and carbon emission futures from a forecasting perspective with implications for environmental sustainability. We employed daily data on natural gas futures prices, crude oil futures prices, carbon futures prices, and Dow Jones energy commodity futures prices from January 2018 to October 2021. For empirical analysis, we applied machine learning tools including traditional multiple linear regression (MLR), artificial neural network (ANN), support vector regression (SVR), and long short-term memory (LSTM). The machine learning analysis provides two key findings. First, the nonlinear frameworks outperform linear models in developing the relationships between future oil prices (crude oil and heating oil) and carbon emission futures prices. Second, the machine learning findings establish that when oil prices and natural gas prices display extreme movement, carbon emission futures prices react nonlinearly. Understanding the nonlinear dynamics of extreme movements can help policymakers design climate and environmental policies, as well as adjust natural gas and oil futures prices. We discuss important implications to sustainable development goals mainly SDG 7 and SDG 12.

Can environmental regulations break down domestic market segmentation? Evidence from China.

While local protectionism and market segmentation owing to fiscal decentralization are not conducive to broad economic development, they may be rational choices on a local scale. Based on a spatial Durbin model, we analyzed the relationship between environmental regulations and market segmentation in China using interprovincial panel data for 2004-2018. The results indicated that the "beggar-thy-neighbor" phenomenon persists in China; environmental regulations have a U-shaped impact on market segmentation, i.e., in most regions, environmental regulation can break down market segmentation. Regions with greater decentralization are better able to promote local market integration through environmental regulation, suggesting that local governments are better able to compensate for market failures when vested with greater power. Hence, we propose that the central government should improve performance evaluation indicators for local governments and grant them greater autonomy; additionally, local governments should increase the intensity of environmental regulations as appropriate, thereby promoting both environmental protection and the unification of domestic markets.

Financial inclusion, renewable energy consumption, and inclusive growth: cross-country evidence.

The COVID-19 pandemic has affected the global economy to varying degrees. Coupled with the widening gap caused by the unbalanced distribution of resources, the sustainability and inclusiveness of economic growth have been challenged. To explore the influencing factors of the level of economic inclusive growth among different countries, we used the spatial Durbin model to analyze the relationship between financial inclusion, renewable energy consumption, and inclusive growth based on panel data of 40 countries from 2010 to 2020. The results indicate a spatial autocorrelation in inclusive growth; financial inclusion and renewable energy consumption both contributed positively to inclusive growth, while industrial structure upgrading played a negative moderating role between domestic renewable energy consumption and inclusive growth. The results of this study provide insights into achieving better inclusive growth and maintaining sustainable and balanced economic development. Based on this, policy recommendations such as expanding the coverage of inclusive finance, optimizing the energy structure, and changing the economic development model are put forward.