Search

Cities have emerged as critical and challenging places for promoting the green and low-carbon transformation of the economy and society in the context of the "dual carbon" targets. Meanwhile, fiscal policy is also an indispensable component of the public policy framework supporting the green and low-carbon transformation. This paper uses the pilot project of "Comprehensive Demonstration Cities for Energy Conservation and Emission Reduction Fiscal Policies" ("ECER-CD" policy) implemented in China since 2011 as a quasi-natural experiment. This research theoretically analyzes and empirically tests the energy conservation and emission reduction effects of fiscal policies supporting green and low-carbon transformation utilizing methodologies such as time-varying DID and multiple periods and multiple groups of DID. The study finds that the pilot cities effectively reverse the behavioural bias of local governments and promote energy conservation and emission reduction efforts. This conclusion holds after undergoing robustness tests such as parallel trend analysis and heterogeneity treatment effects. The effectiveness of the pilot policy is attributed to the upgrade of industrial structure, improvement of energy efficiency, and the effects of green technology innovation. However, cities' geographic location and resource dependency can interfere with the effective implementation of policies. This paper also further explores the environmental dividends and economic consequences of the policy and finds that the pilot cities have certain environmental dividends and do not significantly restrain economic and social development. This study broadens the research perspective on fiscal policies in energy conservation and emission reduction and clarifies the pathways through which fiscal policies promoting green, low-carbon transformation drive energy conservation and emission reduction in cities. Moreover, it also improves the use of the Difference-in-Differences method in evaluating policy effects in scenarios such as the overlapping implementation of pilot policies and policy withdrawal. Finally, this paper proposes policy recommendations to provide more empirical evidence for China to accelerate the green and low-carbon transformation of the economy and society. [ABSTRACT FROM AUTHOR]

Energy quota trading is critical to both energy conservation and the achievement of dual-carbon goals. However, little remains to be done on total factor productivity and economic potential for Chinese industry. The purpose of this paper is to counterfactually model and explore the effects of energy quota trading on total factor productivity and economic potential in industrial sector. We propose a command-controlled DEA model and an energy quota trading DEA model through non-parametric linear programming based on two policy strategies of energy consumption control. We then assess total factor productivity and its growth sources for 35 two-digit industrial sector in China, as well as examine economic potential and optimal path forecasts for carbon peaking. The results show that total factor productivity and economic potential for energy quota trading are higher than those for command-controlled energy policy. Energy-saving effect can be achieved for energy quota trading because the total energy input is lower. Additionally, the sources of productivity growth differ in timing. The average economic potential and total factor productivity of different industries differ due to the heterogeneity of the production conditions and scale of development of individual market players. Moreover, the catch-up emission reduction path with an increase of 8% in output and 4.76% in energy consumption is the optimal carbon peaking path under energy quota trading. Our findings shed valuable light on China's efforts to expand energy quota trading for high-quality development in the context of carbon peaking. • Propose a command-controlled DEA model and an energy quota trading DEA model. • Assess TFP and its growth sources for industrial sector in China. • Compare the impact of different energy policies on economic potential and TFP. • Estimate the energy saving and emission reduction potential of energy quota trading. • Examine the optimal energy-saving and emission-reduction path forecasts for carbon peaking.. [ABSTRACT FROM AUTHOR]

Continuous growth of energy consumption for metro systems has raised the public's concern. Meanwhile, both operators and passengers pursue for efficient metro services, especially during peak hours. This paper aims at designing a bi-objective optimization model and a new solution approach so that the energy conservation of metro trains and less passenger waiting time can be achieved. Firstly, considering complex routes, a multi-particle train operation model with the objectives of punctuality, parking accuracy, energy efficiency and safety provided is established for the minimization of traction energy consumption through investigating the optimal force coefficients and coast positions, while suitable dwell times and headway times of metro trains are found trough building a timetable model, which balances the regenerative energy, transfer and non-transfer passenger waiting time. Secondly, a deep reinforcement learning (DRL) and non-dominated sorting genetic algorithm II (NSGA-II) based two-layer solution approach is introduced for model calculation. Finally, comparison shows that the proposed model can achieve the energy conservation of metro trains by 9.16% with passenger waiting time decreased by 15.88%. More experiments further demonstrate the superiority of the developed model and solution approach. [Display omitted] [ABSTRACT FROM AUTHOR]

Renewable energy consumption has a strong driving force for promoting energy conservation and emission reduction, and is a new way to achieve clean and low-carbon development. Based on this, this paper uses the data of renewable energy consumption, carbon productivity, GDP, population, energy efficiency and technological innovation of 30 provinces in China from 2011 to 2020, and empirically analyzes the impact of renewable energy consumption on carbon productivity in 30 provinces in China, using STIRPAT model, comprehensively considers spatial horizontally and temporal vertically dimension. The empirical results show that: (1) China's carbon productivity shows a significant spatial spillover effect, its impact can spill over to adjacent areas within 1000 km; (2) China's renewable energy power consumption plays a significant role in promoting the improvement of carbon productivity. (3) Renewable energy consumption in the western region plays the most important role in driving carbon productivity. Finally, according to the temporal and spatial relationship between renewable energy consumption and carbon productivity in different regions, we provide specific regional policy suggestions. • The STIRPAT model is employed to explore temporal and spatial impact, while it considers sub regions. • Spatialy, the impact can spill over to adjacent areas within 1000km. Temporally, the impact fluctuates and rises. • The positive role of renewable energy consumption is found in China's western region. [ABSTRACT FROM AUTHOR]

Better digging the sustainable development path of the top five energy consumers (China, the United States, India, Japan and Russia) can serve both domestic economy and global economy to achieve coordinated development with the ever-increasing energy demand. This work has made the following efforts: (1) the Tapio decoupling model was employed to determine the decoupling states between energy consumption and economy; (2) based on the extended Kaya formula and the LMDI method, this work focused on measuring the impact of technological factors and urbanization factor on energy consumption; (3) the LMDI method and the Tapio decoupling model were combined to measure the impact degree of influencing factors on decoupling indicators. The main findings show that from 2001 to 2018: (1) Japan was the country with the most year of strong decoupling, followed by the United States, which indicates that the decoupling status of developed countries was better than that of developing countries. (2) The driving forces behind the energy consumption growth are mainly the effects of R&D scale and urbanization, while the main factors that hinder energy consumption growth were technological factors, including energy intensity effect and R&D efficiency effect. (3) Energy intensity effect was the main driving force for the decoupling process, and the R&D efficiency effect was the biggest obstacle to decoupling energy consumption from economic growth. Finally, this paper proposes that the government can give priority to relying on technological innovation to accelerate the development and promotion of energy-saving and emission-reduction technologies. • Exploring the coordinated development path of energy consumption and economic growth in top five energy consumers. • Extending the traditional LMDI method to measure the impact of technological progress and urbanization on energy consumption. • R&D scale and urbanization promote energy consumption growth, while technological progress inhibits energy consumption growth. • Governments can prioritize technological innovation to speed up energy conservation and emissions reduction. [ABSTRACT FROM AUTHOR]