Forecasting CO2 Emissions Using A Novel Grey Bernoulli Model: A Case of Shaanxi Province in China

Analysis of carbon emission drivers and multi-scenario projection of carbon peaks in the Yellow River Basin

The Yellow River Basin is a key ecological barrier and commercial zone in China, as well as an essential source of energy, chemicals, raw materials, and fundamental industrial foundation, the achievement of its carbon peaking is of great significance for China's high-quality development. Based on this, we decomposed the influencing factors of carbon dioxide emissions in the Yellow River Basin using the LMDI method and predicted the carbon peaking in the Yellow River Basin under different scenarios using the STIRPAT model. The results show that (1) the energy intensity effect, economic activity effect and population effect play a positive role in promoting carbon emissions during 2005-2020. The largest effect on carbon emissions is the population size effect, with a contribution rate of 65.6%. (2) The STIRPAT model predicts that the peak of scenarios "M-L", "M-M" and "M-H" will occur in 2030 at the earliest. The "M-H" scenario is the best model for controlling carbon emissions while economic and social development in the Yellow River Basin. The results of this paper can provide a theoretical basis for the development of a reasonable carbon peak attainment path in the Yellow River Basin and help policy makers to develop a corresponding high-quality development path.

CO2 Emissions Inventory and Its Uncertainty Analysis of China's Industrial Parks: A Case Study of the Maanshan Economic and Technological Development Area

The Chinese government has pledged to peak carbon emissions by 2030 and achieve carbon neutrality by 2060. Industrial parks are the key to achieving the carbon peak and neutrality in industrial sectors. Establishing the CO₂ emissions inventory is the first step to achieve the carbon peak in industrial parks. In this study, a comprehensive CO₂ emissions inventory was established for industrial parks, including three parts: energy consumption, industrial process, and waste disposal. We considered scope 1, 2, and 3 emissions and established an uncertainty analysis framework. Accordingly, scope 1 covered the emissions within the park boundary, scope 2 emissions covered those resulting from electricity and heat usage inside the boundary, and scope 3 included those indirect emissions beyond the boundary. The Maanshan Economic and Technological Development Area (MDA), a typical booming national eco-industrial park of China, was chosen for this case study. The results showed that the MDA CO₂ emissions increased yearly, from 376,836.57 tons in 2016 to 772,170.93 tons in 2021. From the industrial structure perspective, heavy industry contributed the highest emissions. By dividing the emissions into scope 1, 2, and 3, scope 2 could be identified as the largest emissions source. In addition, we conducted inventory uncertainty analyses incorporated by activity levels, emissions factors, and unspecific factors. Overall, these results may promote the establishment of greenhouse gas accounting standards for Chinese industrial parks.

Prediction and Trend Analysis of Regional Industrial Carbon Emission in China: A Study of Nanjing City

Based on the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, the impact factors of industrial carbon emission in Nanjing were considered as total population, industrial output value, labor productivity, industrialization rate, energy intensity, research and development (R&D) intensity, and energy structure. Among them, the total population, industrial output value, labor productivity, and industrial energy structure played a role in promoting the increase of industrial carbon emissions in Nanjing, and the degree of influence weakened in turn. For every 1% change in these four factors, carbon emissions increased by 0.52%, 0.49%, 0.17% and 0.12%, respectively. The industrialization rate, R&D intensity, and energy intensity inhibited the increase of industrial carbon emissions, and the inhibiting effect weakened in turn. Every 1% change in these three factors inhibited the increase of industrial carbon emissions in Nanjing by 0.03%, 0.07%, and 0.02%, respectively. Then, taking the relevant data of industrial carbon emissions in Nanjing from 2006 to 2020 as a sample, the gray rolling prediction model with one variable and one first-order equation (GRPM (1,1)) forecast and scenario analysis is used to predict the industrial carbon emission in Nanjing under the influence of the pandemic from 2021 to 2030, and the three development scenarios were established as three levels of high-carbon, benchmark and low-carbon, It was concluded that Nanjing’s industrial carbon emissions in 2030 would be 229.95 million tons under the high-carbon development scenario, 226.92 million tons under the benchmark development scenario, and 220.91 million tons under the low-carbon development scenario. It can not only provide data reference for controlling industrial carbon emissions in the future but also provide policy suggestions and development routes for urban planning decision-makers. Finally, it is hoped that this provides a reference for other cities with similar development as Nanjing.