Influencing factors of carbon emissions and their trends in China and India: a machine learning method

Simulation of emission reduction path under the path of differentiated energy transformation in China's industrial cities: a case study of Shanghai

The formulation of long-term step-by-step emission reduction plan is an important step for effective scientific emission reduction. This paper takes Shanghai as the research object, constructs PSO-LSTM model on the basis of STIRPAT model, and further constructs three dynamic policy scenarios combined with China's actual situation and makes short-, medium-, and long-term multivariate predictions for them. The study finds that only the improvement of energy consumption structure has a promotion effect on carbon emission reduction, and urbanization, industrial structure, technology level, population, and economic level all have an increasing effect, and secondly, the carbon emission reduction path of Shanghai basically achieves the core objective of steady decrease under the three scenarios. Secondly, under the three scenarios, Shanghai's carbon emission reduction path basically achieves the core objective of steady decline, but the decline in the GU scenario is more significant. It is recommended that Shanghai further adjusts its industrial structure, optimizes its energy consumption structure, promotes technological innovation and progress, and promotes the development of the circular economy model.

Investigating global surface temperature from the perspectives of environmental, demographic, and economic indicators: current status and future temperature trend

Anthropogenic activities have increased atmospheric concentrations of greenhouse gas emissions, which have observably increased global temperature. Recognizing it as one of the most critical issues caused by human activities, this study investigates the effects of environmental, demographic, and economic indicators on global and regional temperature. For this purpose, advanced and powerful machine learning techniques, such as ANN, CNN, SVM, and LSTM, are employed using the data from 1980 to 2018 of the aforementioned regions to predict and forecast global and regional temperatures in Africa, Asia, Europe, North America, and South America. First, the predicted results were found very close to the actual surface temperature, confirming that environmental, economic, and demographic indicators are critical drivers of climate change. Second, this study forecasted global temperature from 2023 to 2050 and regional temperature from 2022 to 2050. The results also predicted a considerable increase in global temperature and regional temperature in the forthcoming years. Particularly, Asia and Africa may experience extreme weather in the future with an increase of more than 1.6 °C. Based on the findings of this study, the major implications have been that maintaining greenhouse gas emissions, balancing economic development, urbanization, and environmental quality while reducing fossil fuel energy consumption will ensure climate mitigation. The findings demand an alteration in human behavior regarding fossil fuel energy consumption to control greenhouse gas emissions, which is the most significant contributor to climate change.

Development of an extended STIRPAT model to assess the driving factors of household carbon dioxide emissions in China

Although the past twenty years have witnessed China's remarkable economic development, the cost in terms of greenhouse gas emissions and a deteriorating environment has been enormous. Numerous studies have revealed the influence of household factors on household carbon dioxide emissions (HCEs) and called for a reduction of HCEs to mitigate climate change, but few have focused on assessing the most significant household driving factors of HCEs. Using statistical data between 2005 and 2019 in Jiangsu, China, this study developed an extended stochastic impact by regression on population, affluence, and technology (STIRPAT) model to assess the most significant driving factors of HCEs. The results show that the most significant driving factors are household size, total population, unemployment, and urbanisation rate. The study found that HCEs are positively impacted by household size while negatively impacted by the unemployment rate. Based on the study's findings, the following suggestions are proposed to lower HCEs: (i) establish an optimal consumption concept to guide residents towards consuming reasonably; (ii) cultivate a low-carbon concept among residents and promote low-carbon emissions living; and (iii) pay close attention to population structure factors and formulate effective measures accordingly. The study provides insightful information on the key driving factors of HCEs, which can facilitate achieving carbon emissions neutrality.

The road to green development: How can carbon emission trading pilot policy contribute to carbon peak attainment and neutrality? Evidence from China

Carbon emission trading is not only a market-based instrument but also one of the government's macro-policies, which is extremely crucial to fulfilling both carbon peak attainment and carbon neutrality goals. For this purpose, this paper adopts a 30-region dataset for the period from 2008 to 2020 in China and employs the difference-in-difference (DID) method to quantify the effect of the carbon emission trading pilot policy (CETP) on carbon emissions on the basis of introducing industrial structure upgrading and green technology innovation as moderating variables. The results show that (1) CETP has a statistically significant dampening effect on carbon emissions, while its carbon emission reduction effect follows a significant strengthening trend as the policy year of CETP implementation is delayed. (2) CETP has a significant carbon emission reduction effect. However, its effect demonstrates a gradual decrease from the eastern to the central and finally to the western regions. (3) CETP can inhibit carbon emissions depending on industrial structure upgrading to a certain extent, and this dependence is significant in the national and eastern regions but not in the central and western regions. (4) CETP's carbon emission reduction effect is dependent on green technology innovation, which is only revealed in the western region and performs as a dampening effect in the national, eastern, and central regions, but not significantly.