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This paper investigates the relationship between digital finance and carbon emissions and explores the ecological effects of digital finance. Based on a panel data of 256 cities in China from 2011 to 2018, this paper investigates the impact of digital finance on carbon emissions and its intrinsic mechanisms. First, digital finance significantly suppresses the intensity of regional carbon emission, and the breadth of coverage, depth of use, and degree of digital support of digital finance together curb regional carbon emissions, with the strongest suppressive effect being the breadth of coverage. In addition, the regression results remain significant after a series of robustness tests. Second, it reveals the potential mechanism of digital finance to curb urban carbon emissions. These mechanisms include the three channels: promoting industrial advancement, green technology innovation, and optimizing labor resource allocation. Third, the heterogeneity test finds that the energy saving and emission reduction effects of digital finance are significantly stronger in non-low-carbon pilot cities with low urbanization rates, confirming the emission reduction utility of digital finance development. Therefore, we should take advantage of digital finance to improve the green development of financial services and adopt diverse policy measures according to local conditions to maximize the ecological effects of digital finance on energy saving and emission reduction. In the context of the development strategy of "carbon peaking and carbon neutral," this study has some implications for management in developing a regional green development system supported by digital finance. [ABSTRACT FROM AUTHOR]

In 2023, China's crude steel production amount reached 1.019 billion tons, and the energy consumption of China's steel industry amount reached 561 million tons of coal. China's steel industry, with its dominant reliance on coal for energy and the primary use of blast furnaces and converters in production processes, as well as its massive output, has become the main field for achieving China's "carbon peaking" and "carbon neutrality" goals. Firstly, this article summarizes the current production status of the steel industry and the situation of carbon emissions in the steel industry. Secondly, it discusses the dual-carbon policies based on the national and steel industry levels and outlines the future directions for China's steel industry. Subsequently, it analyzes the current state of research and application of mature and emerging low-carbon technology in China's steel industry and details the low-carbon plans of China's steel companies using the low-carbon technology roadmaps of two representative steel companies as examples. Finally, the article gives policy suggestions for the further carbon reduction of China's steel industry. The purpose of this paper is to show the efforts and contributions of China's steel industry to the early realization of its "carbon peaking" and "carbon neutrality" goals. [ABSTRACT FROM AUTHOR]

How to achieve the "double carbon" goal in energy systems has been the concern of governments. Integrated energy system (IES) is affected by climate change during his operation, in order to study the impact of climate change on IES and achieve the "double carbon" goal in energy systems, this paper proposes an integrated machine learning(IML) to forecast the long-term load, then investigates IES costs and carbon emissions in relation to climate, followed by the establishment of carbon peak energy system(CPES) and carbon neutral energy system(CNES), finally the honey badger algorithm is used to optimize the configuration of CPES and CNES. The results show that: IML can accurately make load forecasts. Under climate change, changes in load reduce the cost and carbon emissions of IES, and changes in equipment efficiency increase the cost and carbon emissions of IES. When both are considered, the cost and carbon emissions of IES increase by 1.18% and 0.92% per decade respectively. The costs of CPES and CNES increase by 0.93% and 1% respectively for every 10 years earlier than the year of achievement. To meet China's "double carbon" goal, CPES and CNES need to increase their costs by 1.97% and 2% respectively. • Integrated machine learning proposed for long-term load forecasting. • Studied the impact of system equipment efficiency and load with climate change. • Proposing carbon peaking energy system to achieve the carbon peak goal. • Proposed carbon-neutral energy system to achieve carbon neutrality goal. • The increased costs of achieving carbon peak and carbon neutral were studied. [ABSTRACT FROM AUTHOR]

A study linking the two economic and social phenomena of rural labor force migration and energy transition can help analyze the underlying causes of rural "Energy Poverty". However, how off-farm employment affects household low-carbon energy consumption and its potential mechanisms requires further research. Using 1351 sampled rural households from the "Rural Energy, Population Transfer and Well-being" survey in 2018 and 2021 to explore response mechanisms through which off-farm employment can influence low-carbon energy intensity. Utilizing the multivariate regression, Sobel test, and moderating effect test, the results demonstrate that off-farm employment, including short-term and long-term off-farm employment, significantly increases the intensity of low-carbon energy use among rural households. Specifically, long-term off-farm employment tends to have a greater positive contribution to the low-carbon energy intensity than short-term off-farm employment. Furthermore, off-farm employment can affect household low-carbon energy intensity through the total income, and effect of the surrounding people in the off-farm employment process also increases their consumption intensity. The research reveals that the rural energy revolution under the constraints of "Carbon Neutral" and "Carbon Peak" should relate to the off-farm development of rural households to achieve "Precise Energy Poverty Alleviation". [ABSTRACT FROM AUTHOR]