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The explosive growth of the logistics industry has led to an increase in energy consumption and carbon emissions. To reduce emissions and increase the efficiency of the logistics industry, we studied the driving factors and decoupling effects of carbon emissions of logistics industry (LICE). First, an energy coefficient method is used to calculate the LICE. Second, the Logarithmic Mean Divisia Index (LMDI) decomposition method is used to decompose the driving factors of LICE into five types. Third, the decoupling model is used to explore the decoupling relationship between economic growth and LICE. Considering Anhui Province as an example, this study describes the method's implementation process based on a comparison of the four Yangtze River Delta provinces and cities. The results indicate that the growth rate of LICE in Anhui Province has decreased during the research period, from 9.7% in 2013 to 2.1% in 2021; however, the Tapio decoupling elasticity has been approximately 0.4 for the last 2 years, remaining in a weak decoupling stage from economic development. The LMDI decomposition results indicate that the average contribution of economic level to carbon emissions is 1.763. This study proposes some solutions and recommendations for the logistics industry's low-carbon development to offer methodological and theoretical support for LICE research. [ABSTRACT FROM AUTHOR]

As industry is the world's leading carbon emitter, promoting industrial carbon reduction is of key significance to carbon peak and carbon neutrality. Using a data-driven method, based on the collection and processing of relevant data from statistical yearbooks and others, we analyze the efficiency and amount of carbon emission of each industrial sector after processing multi-dimensional data by the improved IPCC EF method of calculating carbon emissions. In addition, we adopt the LMDI decomposition method for data modeling to measure the contribution of energy efficiency, industrial structure, GDP per capita, and population size to carbon emission changes, to identify targets for industrial carbon reduction, and to propose a targeted optimization path for carbon emission. We show how the method is implemented by taking the statistics of Anhui Province from 2010 to 2019 as an example and advises on an optimization path for carbon emission in Anhui Province. This study is of both theoretical and practical significance as it provides theoretical and methodological support for the low-carbon development of the regional industry, and provides a reference for other countries and regions to explore the path of low-carbon and environment-friendly green transformation and upgrading. [ABSTRACT FROM AUTHOR]

With the increasing severity of the global energy crisis and environmental pollution, there is an urgent need to change the economic development model driven by certain factors and the investment scale and pursue science- and technology-driven innovative development. This study aims to improve the efficiency of scientific and technological innovation and promote the high-quality development of regional industrial enterprises. It constructs a data-driven DEA-Malmquist evaluation model to evaluate and optimize regional industrial enterprises' scientific and technological innovation efficiency. First, we collect the panel data of regional industrial enterprises' scientific and technological innovation input-output indexes. Second, we use the Pearson correlation coefficient method to identify and construct the evaluation index system of regional industrial enterprises' scientific and technological innovation efficiency. Third, we build a DEA-Malmquist evaluation model to quantitatively evaluate regional industrial enterprises' scientific and technological innovation efficiency from static and dynamic aspects. Finally, we verify the feasibility and effectiveness of the method using statistical data on scientific and technological innovation and development of Anhui industrial enterprises from 2011 to 2019 and put forth targeted countermeasures and suggestions. This study provides theoretical and methodological support for the sustainable development of industrial enterprises. [ABSTRACT FROM AUTHOR]

This study aims to promote the high-quality development of regional agriculture. This study proposes a data-driven method for regional agricultural analysis and evaluation. Based on the data collection and processing related to regional agricultural development, the location entropy index was used to analyze the industrial agglomeration level, and the shift-share model was constructed to evaluate the industrial structure and competitiveness. Based on the above analysis and evaluation, high-quality development policy suggestions for regional agriculture were provided. Taking the agricultural development of the Yangtze River Delta from 2010 to 2019 as an example, this study shows the implementation process of the method. From the perspective of high-quality agricultural development in the Anhui Province, this paper proposes policy suggestions on industrial structure adjustment and promoting competitiveness. This study provides theoretical and methodological support for the development of high-quality regional agriculture. [ABSTRACT FROM AUTHOR]

The online version of the original article can be found at https://doi.org/10.1007/s11356-021-17358-w B Correction to: Environmental Science and Pollution Research b https://doi.org/10.1007/s11356-021-17358-w The correct affiliations of Fan Liu, Cui Wang and Conghu Liu is presented in this paper. Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. [Extracted from the article]