12/8/2023 0 Comments Urban realms model vs sector modelJiang H, Yin J, Qiu Y, Zhang B, Ding Y, Xia R (2022) Industrial carbon emission efficiency of cities in the Pearl River Basin: spatiotemporal dynamics and driving forces. Ishida H (2014) The effect of ICT development on economic growth and energy consumption in Japan. ![]() Hu X, Guo P (2022) A spatial effect study on digital economy affecting the green total factor productivity in the Yangtze River Economic Belt. Holmes TJ (2010) Structural, experimentalist, and descriptive approaches to empirical work in regional economics. He Y, Xie C (2022) Measurement, decomposition and emission reduction effects of digital global value chains. Hao Y, Ba N, Ren S, Wu H (2020) How does international technology spillover affect China’s carbon emissions? A new perspective through intellectual property protection. Hampton SE, Strasser CA, Tewksbury JJ, Gram WK, Budden AE, Batcheller AL, Duke CS, Porter JH (2013) Big data and the future of ecology. ![]() Habiba UMME, Xinbang C, Anwar A (2022) Do green technology innovations, financial development, and renewable energy use help to curb carbon emissions? Renew Energy 193:1082–1093. Goldfarb A, Tucker C (2019) Digital economics. Gelernter J, Carley KM (2015) Spatiotemporal network analysis and visualization. Gao P, Yue S, Chen H (2020) Carbon emission efficiency of China’s industry sectors: From the perspective of embodied carbon emissions. Gao W, Peng Y (2022) Energy saving and emission reduction effects of urban digital economy: technology dividends or structural dividends? Environ Sci Pollut Res 30:36815–36871. įeng Y, Zhu A, Liu P, Liu Z (2022) Coupling and coordinated relationship of water utilization, industrial development and ecological welfare in the Yellow River Basin, China. ĭuque-Cartagena T, Mundstock E, Dala Bernardina Dalla M, Vontobel Padoin A, Cañon-Montañez W, Mattiello R (2023) The role of environmental pollutants in body composition: systematic review and meta-analysis. ĭong F, Hu M, Gao Y, Liu Y, Zhu J, Pan Y (2022) How does digital economy affect carbon emissions? Evidence from global 60 countries. ĭaud SNM, Ahmad AH (2022) Financial inclusion, economic growth and the role of digital technology. Ĭoccia M, Bontempi E (2023) New trajectories of technologies for the removal of pollutants and emerging contaminants in the environment. Ĭhen P (2020) Effects of the entropy weight on TOPSIS. īertani F, Ponta L, Raberto M, Teglio A, Cincotti S (2020) The complexity of the intangible digital economy: an agent-based model. īanalieva ER, Dhanaraj C (2019) Internalization theory for the digital economy. Īmano A (1964) Biased technical progress and a neoclassical theory of economic growth. ![]() This study provides a certain reference for the green and low-carbon development of industry in China and other developing countries in the digital economy era.Īhmed MA-O, Mohamed GO, Bishoy ES (2023) Analysis of the economic and technological viability of producing green hydrogen with renewable energy sources in a variety of climates to reduce CO 2 emissions: a case study in Egypt. ![]() The condition β-convergence result indicates that underdeveloped regions can narrow the gap between their ICEE and that of developed regions by utilizing their resource endowments, industrial structure, human capital, and other conditions, improving emission reduction measures and policies. The fluctuation of China’s ICEE has consistent σ-convergence and β-convergence, and the convergence effect is higher with the introduction of the DE than without it. The coupling coordination degree between DE and ICEE in the eastern, central, and northeastern regions has reached an intermediate level or above, with the highest degree in the eastern region. Currently, the coupling coordination degree between the development of China’s DE and ICEE has reached the level of primary coordination or above. The results show that the ICEE and DE in various provinces of China exhibit obvious spatial heterogeneity and spillover effects. The coupling coordination model and convergence model were adopted to explore the development trend of coupling coordination between DE and ICEE. In the study, a multidimensional indicator system was established to evaluate DE, and spatiotemporal analysis and network analysis methods were used to reveal the dynamic evolution characteristics of DE and ICEE. Exploring the coupling coordination between China’s digital economy (DE) and industrial carbon emission efficiency (ICEE) is of great significance for achieving sustainable development goals.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |