Carbon emissions dynamic simulation and its peak of cities in the Pearl River Delta Urban Agglomeration

The Hu-Bao-O-Yu urban agglomeration is an important energy exporting and high-end chemical base in China, and is an important source of carbon emissions in China. The early achievement of peak carbon emissions in this region is particularly crucial to achieving the national carbon emission reduction targets. However, there is a lack of multi-factor system dynamics analysis of resource-dependent urban agglomerations in Northwest China, as most studies have focused on single or static aspects of developed urban agglomerations. This paper analyses the relationship between carbon emissions and their influencing factors, constructs a carbon emission system dynamics model for the Hu-Bao-O-Yu urban agglomeration, and sets up different single regulation and comprehensive regulation scenarios to simulate and predict the carbon peak time, peak value, and emission reduction potential of each city and urban agglomeration under different scenarios. The results show that: (1) Hohhot and Baotou are expected to reach peak carbon by 2033 and 2031 respectively, under the baseline scenario, while other regions and the urban agglomeration will not be able to reach peak carbon by 2035. (2) Under single regulation scenarios, the effect of factors other than the energy consumption varies across cities, but the energy consumption and environmental protection input are the main factors affecting carbon emissions in the urban agglomeration. (3) A combination of the economic growth, industrial structure, energy policy, environmental protection, and technology investment is the best measure to achieve carbon peaking and enhance the carbon emission reduction in each region as soon as possible. In the future, we need to coordinate the economic development, energy structure optimisation and transformation, low-carbon transformation of industry, strengthen research on carbon sequestration technology, and further increase the investment in environmental protection to make the Hu-Bao-O-Yu urban agglomeration a resource-saving urban agglomeration with an optimal emission reduction.

Global climate continues to warm; by reducing carbon emission (CE) to cope with climate warming has become a global consensus. The influencing factors of CE exhibit diversification and spatial characteristics, and the complexity of the CE system poses challenges to green and low-carbon development and the realization of China's dual-carbon goals. Taking the Pearl River Delta urban agglomeration as an example, this study explored the influencing factors of CE and designed emission reduction schemes with the help of multi-scale geographically weighted regression (MGWR). Based on this, the system dynamics model was used to construct a CE system framework considering multi-dimensional driving factors, so as to combine the complex CE system with the emission reduction countermeasures considering spatial heterogeneity, and realize the dynamic simulation of CE reduction policies. The results showed that the urban agglomeration as a whole will reach carbon peak by 2025. Shenzhen, Zhuhai, and Dongguan have achieved carbon peak before 2020, while other cities will reach carbon peak by 2025-2030. The government policy constraints can effectively curb CE, but if government constraints were relaxed, CE will rise and individual cities will not reach carbon peak. Comprehensive CE reduction policies are better than a single CE reduction policy. The study found that this model framework provides a systematic analysis of carbon reduction strategies for urban agglomerations, offering decision-makers various combinations of economic development and green low-carbon objectives. This will further contribute to a multi-faceted mitigation of high emission in urban agglomeration and promote regional sustainable development.

Transportation carbon emission reduction potential and mitigation strategy in China

Evaluation of carbon emission efficiency and reduction potential of 336 cities in China

The farming-pastoral ecotone has an important strategic place in the energy supply and ecological layout of China. Thus, exploring the spatial and temporal variation characteristics of carbon emissions in this region will help to deeply understand the information on the historical carbon emissions in China's energy production bases and provide data references for the formulation of differentiated emission reduction policies and the promotion of regional energy-saving and carbon-reducing measures, which is of great significance for the realization of low-carbon economic development. This study constructed a spatialization model of carbon emissions based on land use, night lighting, and provincial energy consumption data； explored the spatiotemporal changes and aggregation characteristics of carbon emissions in the farming-pastoral ecotone from 1995 to 2020 using the global Moran's index and hotspot analysis； and then combined it with the slack-based measure model to calculate the carbon emission efficiency and emission reduction potential of each city from 2010 to 2020 and classify cities to propose a differentiated emission reduction path. The results showed that, firstly, the estimated results at the prefectural city level of the carbon emission spatialization model constructed in this study with multi-source data could reach an R2 of 0.92 for a linear fit. Secondly, the total carbon emissions in the farming-pastoral ecotone increased from 176.29 million tons in 1995 to 1 014.51 million tons in 2020. However, the carbon emission intensity and growth rate both decreased, which was related to adjusting the energy structure and improving energy efficiency. Regarding spatial distribution, the cities with high carbon emissions over time were Datong, Baotou, and Yulin in order. Thirdly, the carbon emissions in the study area showed a significant global spatial positive correlation at the county level, with the hot spots mainly located at the junction of Shanxi, Shaanxi, and Inner Mongolia, while the cold spots were extended from Yanan City to Qingyang and Guyuan City after 2010. Finally, based on the differences in carbon emission efficiency and reduction potential, cities could be classified into four types： "high-efficiency and high potential," "low-efficiency and high potential," "high-efficiency and low potential," and "low-efficiency and low potential" to implement targeted emission reduction strategies.

In China, manufacturing is the industry that consumes the most energy and emits the most carbon, and the effect of emission reduction on the process of reaching carbon peaking and carbon neutrality is decisive. The existing research on the driving factors of manufacturing carbon emissions has not analyzed the specific structural characteristics of manufacturing carbon emissions from the perspective of industrial relevance, and little attention has been paid to the discussion of carbon emission reduction paths of different manufacturing sectors from the perspective of final demand. This study examines the direct carbon emissions and carbon emissions from final demand in China's manufacturing sector, and decomposes the carbon emissions from final demand into six distinct components using input-output analysis. In addition, this study examines the carbon emission path in manufacturing production activities, as well as the carbon emission reduction potential and scenario prediction of the factors influencing manufacturing carbon emissions. In 2018, the direct carbon emissions and carbon emissions from final demand were approximately 4.61 billion tons and 3.50 billion tons, respectively. Meanwhile, direct and indirect spillovers accounted for 62.1% and 23.1% of carbon emissions from final demand, respectively. Using the carbon emission transfer route map of the manufacturing industry, the direction and amount of carbon emission transfer from various energy sources can be accurately determined. The CR scenario predicts that the manufacturing industry will reach its carbon peak between 2025 and 2030, with a corresponding peak between 4.02 and 4.06 billion tons, and that carbon emissions in 2060 will be 40% lower than in 2018.

It is of great significance to formulate differentiated carbon emission reduction policies to clarify spatio-temporal characteristics and driving factors of carbon emissions in different cities and cities at different scales. By fitting nighttime light data (NTL) of long time series from 2000 to 2020, a carbon emission estimation model of Pearl River Delta urban agglomeration at city, county, and grid unit levels was built to quickly and accurately estimate carbon emission in the Delta cities above county level. Combining spatial statistics, spatial autocorrelation, Emerging Spatio-Temporal Hotspot Analysis (ES-THA), and Theil index (TL), this study explored the spatio-temporal differentiation of urban carbon emissions in the Delta and used a geographical detector to determine the influencing factors of the differentiation. The results of the study showed that NTL could replace a statistical yearbook in calculating carbon emissions of cities at or above county level. The calculation error was less than 18.7385% in the Delta. The three levels of carbon emissions in the Delta increased in a fluctuating manner, and the spatial distribution difference in carbon emissions at the municipal and county levels was small. Therefore, a combination of municipal and county scales can be implemented to achieve precise emission reduction at both macro and micro levels. The central and eastern parts of the agglomeration, including Guangzhou (Gz), Shenzhen (Sz), Zhongshan (Zs), and Huizhou (Hz), were a high-value clustering and spatio-temporal hot spots of carbon emissions. Zhaoqing (Zq) in the northwestern part of the agglomeration has always been a low-value clustering and a spatio-temporal cold spot because of its population, economy, and geographical location. The carbon emission differences in the Delta cities were mainly caused by carbon emission differences within the cities at the municipal level, and the cities faced the challenge of regional differences in the reduction in per capita carbon emissions. As the most influential single factor, spatial interaction between economic development and various factors was the main driving force for the growth of carbon emissions. Therefore, the results of this study provide a scientific theory and information support for carbon emission estimation and prediction, differentiated emission reduction measures, and carbon neutrality of cities in the Delta.

Due to the emission of carbon dioxide and other greenhouse gases, the global climate is warming. As the world’s biggest emitter of carbon emissions, China faces a more severe challenge in reducing carbon emissions than developed countries. A reasonable prediction of the carbon peak in China will help the government to formulate effective emission reduction paths. This paper analyzes the changes in carbon emissions in China from 2004 to 2020, uses the STIRPAT model and scenario analysis method to predict carbon emissions from 2021 to 2030, and then calculates the carbon efficiency during carbon peaking to select the most effective carbon peak path for China. The results show that China’s carbon emissions increased year by year from 2004 to 2020. Under the baseline scenario, China is unlikely to reach its carbon peak before 2030. Under the regulatory scenarios, China can reach its carbon peak before 2030. The peak values from high to low are seen with the rapid development-weak carbon control scenario, rapid development-intensified carbon control scenario, slow development-weak carbon control scenario and slow development-intensified carbon control scenario, respectively. Correspondingly, China will peak its carbon emissions in 2029, 2028, 2028 and 2028, respectively, according to these scenarios. The carbon efficiency under the rapid development-weak carbon control scenario is the highest, which means that accelerating the growth rate of population, GDP and urbanization while moderately carrying out the transformation of industrial structure and energy structure is an effective way to achieve the goal of “carbon peak by 2030”.

In the context of reaching peak carbon emissions, it is crucial to develop carbon reduction strategies for high-energy-consuming industries as part of a broader societal transition from dependence on high-pollution energy sources to low-pollution alternatives. This study focuses on carbon emission reduction in the non-ferrous metal industry, which is known for its significant energy consumption. It employs the Logarithmic Mean Divisia Index (LMDI) model to conduct empirical analyses from three perspectives: carbon emission decomposition, regionalization analysis, and carbon emission prediction. The objective is to explore the carbon emission characteristics of high-energy-consuming industries in China and provide theoretical support for future policies aimed at reducing carbon emissions in these industries. The findings reveal that the economic scale of the non-ferrous metal industry has a positive correlation with carbon emissions, while carbon emission coefficients exhibit a negative correlation. Moreover, in the prediction scenarios considered, the increase in carbon emissions resulting from the economic-scale factor accounted for 75.28%, 87.46%, and 65.21% respectively, indicating that it has the most significant influence among all factors analyzed. The study further demonstrates that under stable and active emission reduction scenarios, the future potential for carbon dioxide emission reduction in the non-ferrous metal industry is estimated to reach 858.47 million tons and 1384.65 million tons, respectively. These figures represent twice and three times the emissions recorded in 2021. By analyzing the factors influencing emission reduction, targeted regulations can be implemented to develop practical and effective strategies for reducing carbon emissions in the industry. From the analysis conducted, it can be deduced that high-energy-consuming industries, particularly the non-ferrous metal industry, exhibit relatively high levels of carbon emissions. Consequently, it is imperative to implement proactive measures to reduce these emissions. Additionally, the industry’s carbon emissions are heavily influenced by changes in economic scale due to its high dependence on it. This highlights the importance of considering economic factors when devising strategies to mitigate carbon emissions. Furthermore, the potential for improvement in the non-ferrous metal industry’s energy structure and carbon emission coefficients is limited. Simply relying on technological innovation alone may not suffice to achieve significant emission reduction goals. Therefore, it becomes crucial for the government to develop tailored emission reduction targets and policies based on the industry’s specific circumstances to attain optimal results.

This paper analyzes the spatial and temporal distribution of CO2 emissions intensity and energy intensity in China by using spatial measuring method from 2000 to 2013 and estimates the potential for CO2 emissions reduction. The results obtained in this study include: (1) Both CO2 emissions intensity and energy intensity are declining; (2) the spatial distribution of carbon emission intensity and energy intensity in China shows the characteristics of lower from north to south; (3) China’s total growth of energy consumption and carbon emissions is clearly slowing, which will peak before 2030; the carbon emission reduction potential in China is great with 167,316.91 million tons, and Shanxi, Inner Mongolia and Hebei have the greatest potential to reduce CO2 emissions with 29,885.8 Mt, 32,704.49 Mt and 34,222.1 Mt, respectively; (4) the differences of CO2 emissions intensity and energy intensity among provinces are distinctive. This study can provide a reference for the sustainable development of China’s energy and environment.

A graph-factor-based random forest model for assessing and predicting carbon emission patterns - Pearl River Delta urban agglomeration

Major US electric utility climate pledges have the potential to collectively reduce power sector emissions by one-third

The digital economy is an important driving force for China’s economic transformation and upgrading, while ‘carbon peak and carbon neutrality’ is the target vision to be achieved in China. As an important support area for the rise of central China, it is an urgent question to investigate whether the development of the digital economy in Jiangxi Province can contribute to the realization of carbon emission reduction. Based on the panel data of 11 urban areas in Jiangxi Province from 2011 to 2020, the article empirically investigates the impact of the digital economy on regional carbon emissions and its mechanism of action by using the two-way fixed-effect model, mediated-effect model, and panel threshold-effect model. The study shows that, firstly, the digital economy has a significant carbon emission reduction effect, and the conclusion still holds after a series of endogeneity and robustness tests. Second, the carbon emission reduction effect of the digital economy differs among different regions, and the carbon emission reduction effect of the digital economy is more obvious in northern Ganzhou and regions with high carbon emission levels. Again, the intermediary effect suggests that the digital economy can promote carbon emission reduction by strengthening the control of foreign investment introduction. Finally, the panel threshold effect analysis with urbanization as the threshold variable shows that there is a non-linear correlation between the effects of the digital economy on carbon emissions. The findings of this paper provide a feasible path and policy reference for Jiangxi Province’s digital economy to contribute to carbon emission reduction goals.

In order to study the impact of the "carbon peak and neutrality" mode on future power generation and the environment in China, a Verhulst gray model was established to predict the development of the power generation industry from 2021 to 2060 under the non-"carbon peak and neutrality" mode. In addition, based on the "China 2030 Energy and Power Development Planning Research and 2060 Outlook Report," the development of the power generation industry from 2021 to 2060 under the "carbon peak and neutrality" mode was obtained, and the development scenarios of the future power generation industry in China under two models were compared and studied. The emission factors and emission reduction factors of CO2, SO2, NOx, PM, PM10, and PM2.5 were constructed through the conservation of elements and the generating performance standard, and then four environmental benefits A1-A4 were defined. The results showed that the installed capacity of thermal power will reach the carbon peak in 2026 under the "carbon peak and neutrality" mode. To achieve the carbon neutralization, the installed capacity of thermal power will be reduced by an average of 28 million kilowatts per year after 2026, and the installed capacity of renewable energy generated is required to increase by 154 million kilowatts per year after 2020. Compared with that in the non-"carbon peak and neutrality" mode, the installed capacity of thermal power generation will be greatly reduced, and the installed capacity of renewable energy power generation will be greatly increased under the "carbon peak and neutrality" mode, resulting in huge A1 and A2 environmental benefits. In the next four decades, the cumulative emission reductions in CO2, SO2, NOx, PM, PM10, and PM2.5 thermal power generation A1 are predicted to be 6.64×1010 tons, 1.54×107 tons, 1.55×107 tons, 3.18×106 tons, 1.71×106tons, and 2.23×105 tons, respectively. The cumulative emission reductions of renewable energy power generation A2 will be 5.77×1010 tons, 1.64×107 tons, 1.42×107 tons, 2.86×106 tons, 1.54×106 tons, and 2×105 t tons, respectively. Under the "carbon peak and neutrality" mode, compared with those from coal-fired power generation, the environmental benefits A3 and A4 produced by the relative cleanliness of renewable energy and nuclear power indicated that the cumulative emission reductions (A3+A4) in clean energy power generation of CO2, SO2, NOx, PM, PM10, and PM2.5 in the next four decades will be 3.014×1011 tons, 7.292×107 tons, 7.119×107 tons, 1.454×107 tons, 7.827×106tons, and 1.018×106 tons, respectively.

Carbon emissions of water supply systems in China: Characteristic, right, right balance and reduction potential assessment