5/7/2023 0 Comments Sms plus uruguay![]() ![]() Building on the “center-periphery” framework, we further calculate the regional energy efficiency gap and investigate the impact of regional integration on the regional energy efficiency gap through the generalized moment method (GMM). This paper applies the extended stochastic frontier analysis (SFA) method that incorporates time-varying, time-invariant and city heterogeneous characteristics to estimate the city-level energy efficiency in China from 2005 to 2017. Although studies have analyzed the influencing factors of the regional energy efficiency gap, the impact of regional integration on the regional energy efficiency gap remains untested. Prevalent huge efficiency gaps are not advantageous for the improvement of the region's overall energy efficiency. Improving energy efficiency is essential for energy conservation, emissions reduction, and sustainable development. The findings provide guideline for achieving a low-carbon development and carbon neutrality from a regional green productivity perspective. It indicates that environmental regulations help to facilitate the convergence of China’s green productivity, narrowing the gap between the regional green economic development. Moreover, the convergence rate of China’s green productivity increases with the addition of environmental regulation variable, and so the corresponding convergence time decreases. In terms of regional effects, the results indicate that the green productivity of the eastern and western regions demonstrates club convergence, implying a more balanced green economic development. When taking into account spatial factors, China’s green productivity demonstrates a significant conditional β-convergence. We found that overall, since 2001, China’s green productivity has demonstrated a continuous upward trend. Using a spatial panel data model, we empirically analyzed the conditional β-convergence of China’s green productivity. This study employs the slacks-based measure directional distance function (SBM-DDF) approach and the Malmquist-Luenberger (ML) index to calculate green productivity and its components of 30 provinces in China between 2001‒2018. Green productivity measures the quality of economic growth with consideration for energy consumption and environmental pollution. It requires economic recovery without compromising on the environment, implying a critical role that green productivity plays in achieving the carbon neutrality goal. Low-carbon economic development is at the heart of the post-pandemic green recovery scheme worldwide. Practicable policies to improve industrial energy efficiency in China are suggested and applicable to other emerging economies. In addition, the positive effects of FUS on industrial energy efficiency is significant only at low levels of FUS. The results show that 1) city-level industrial energy efficiency has increased from 0.4133 in 2005 to 0.4461 in 2017, mainly driven by peripheral cities rather than core cities 2) FUS has a significantly positive impact on industrial energy efficiency in full sample and peripheral cities 3) FUS reduces the negative effects of city scale and the positive effects of investment and enhances the positive effects of transportation. Hierarchical linear model (HLM) is used for the estimation of the nested data with cluster-level and city-level, which reduces the bias by nested estimation. Utilizing prefecture-level city data from 2005 to 2017, this paper adopts an extended stochastic frontier analysis (SFA) model to measure industrial energy efficiency. To fill this gap, this study explores the impact of FUS on industrial energy efficiency. As a future trend of urban specialization, the importance of functional urban specialization (FUS) in improving industrial energy efficiency is ignored. Previous literatures have focused on the impact of sectorial urban specialization on energy efficiency. ![]()
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