Ensemble intelligence prediction algorithms and land use scenarios to measure carbon emissions of the Yangtze River Delta: A machine learning model based on Long Short-Term Memory.

Land use in urban agglomerations is the main source of carbon emissions, and reducing them and improving land use efficiency are the keys to achieving sustainable development goals (SDGs). To advance the literature on densely populated cities and highly commercialized regions, this research evaluates the total-factor carbon emission efficiency index (TCEI) of 27 cities in China's Yangtze River Delta (YRD) urban agglomeration at different stages from 2011 to 2020 using two-stage dynamic data envelopment analysis (DEA). The study carries out regression analysis and a long-short-term memory model (LSTM) to respectively filter out the factors and predict TCEI.

Green transformation and performance synergy efficiency of china's thermal power enterprises on the basis of the environmental tax burden.

Global climate change and the collection of environmental protection taxes are accelerating the green transformation of thermal power enterprises. This study selected Chinese thermal power listed companies as samples and used a dynamic three-stage (operational, green transformation, and market performance) network DEA model to evaluate their transformation efficiency and corporate performance. This paper incorporates targeted indicators such as ESG (environment, society, governance) and stock prices into the model and conducts a comparative study on the basis of macro policies and the geographical location of the enterprise.