Research on coal demand forecast and carbon emission reduction in Shanxi Province under the vision of carbon peak

Abstract

Facing the increasingly severe climate situation, China strives to improve its Nationally Determined Contributions, promising to reach its carbon peak by 2030. Accurately predicting the future demand quantity and changing the trends of coal resources is the key to maintaining national energy security and achieving the goal of “carbon peak” and is also an important research topic in the future. To improve the prediction accuracy, this study sorts out eight common factors affecting the coal demand from the aspects of the economy, population, and energy. The grey relational analysis method was used to describe the degree of importance of each factor and screen out the relatively vital factors. The system dynamics model for coal demand in Shanxi Province was also established. The coal demand and carbon emission trends in Shanxi Province from 2021 to 2030 were predicted under business-as-usual, low-speed and high-speed development scenarios. The study results show that: 1) During the 14th Five-Year Plan to 15th Five-year Plan, the trend of coal demand has changed from increasing to decreasing. Under the three scenarios, the coal demand in 2021 is expected to be 372.03 million tons, 365.97 million tons, 360.04 million tons, and it is expected that the coal demand will peak in 2025, and will reach 394.77 million tons, 390.27 million tons, and 385.66 million tons under the three scenarios, respectively. 2) With the development of population and economy, the total energy demand shows a continuously increasing trend. It is estimated that the total energy consumption will reach 237.46-242.93 million tons of standard coal in 2030, and the total energy demand will continue to grow in the foreseeable future. 3) The carbon dioxide emission is closely related to the coal demand, showing a similar variation trend. Under the business-as-usual development scenario, carbon emissions in 2021 are 699.62 million tons, and carbon emissions are expected to reach 681.12 million tons in 2030. The low-speed development and high-speed development scenarios have different degrees of energy saving and emission reduction benefits. Compared with the business-as-usual development scenario in 2030, the carbon dioxide emissions will be reduced by 1.64 million tons and 3.56 million tons respectively. Finally, we put forward important policy measures to promote the economic transformation and upgrading of Shanxi Province and accelerate the realization of the “carbon peak” goal.

Forecast of Transportation CO2 Emissions in Shanghai under Multiple Scenarios

“…A forecast of transportation CO 2 emissions can help us understand the development trend and support the policy making of carbon emission reduction. Researchers have forecasted China's transportation CO 2 emissions across the country [12][13][14] for provinces such as Jiangsu [15,16], Hubei [17][18][19], Hebei [20], Shandon [21], Shaanxi [22], Qinghai [23], Jilin [24], and Hainan [25] and cities such as Beijing [26,27] and Tianjin [28] using the Kaya model [13], the STIRFDT(Stochastic Impacts by Regression on Population, Affluence, and Technology) model [1,17,18,23,[29][30][31], the LEAP (Long-range Energy Alternatives Planning System) method [32], the linear regression method [14], the gray model [20,22,33,34], the LMDI (Logarithmic Mean Divisia Index) method [35,36], the machine-learning method [15,27,37], the system dynamics method [28,[38][39][40], and so on. These methods are often used in combination with a scenario analysis to make predictions [1,13,[16...…”

Spatiotemporal Variation and Dynamic Simulation of Ecosystem Carbon Storage in the Loess Plateau Based on PLUS and InVEST Models

“…Increased carbon storage in terrestrial ecosystems can effectively reduce greenhouse gases and is generally considered to be one of the most economically viable ways to reduce carbon sequestration [2,3]. As the most important manifestation of human activities, land use change leads to changes in carbon storage in terrestrial ecosystems [4,5]. Therefore, exploring the impact of land use change on terrestrial ecosystem carbon storage under the global 1.5 • C temperature control target and revealing the land use pattern and its carbon storage changes under different scenarios can help improve the carbon sequestration potential of terrestrial ecosystems from the Land 2023, 12, 1065 2 of 18 perspective of land use structure optimization, which is conducive to providing useful references for the decision-making of regional ecological environmental protection and carbon reduction and sequestration policies.…”

Research on a Carbon Emission Prediction Method for Oil Field Transfer Stations Based on an Improved Genetic Algorithm—The Decision Tree Algorithm

“…Yan [19] used the STIRPAT model to predict carbon emissions in the blue economic zone of the Shandong Peninsula, and the relationship between population, energy intensity, and carbon emissions was analyzed by the ridge regression method. At the industry level, many scholars have studied the timing of carbon emissions peaking in industries such as electricity [20], transportation [21], construction [22], and industry [23] and control measures to promote carbon peaking. Some authors [24][25][26][27][28][29] argue that population levels are strongly correlated with CO 2 emission levels.…”