基于小尺度的高寒牧区土地碳排放估算——以甘南州合作市为例

Analyzing the spatiotemporal differences in land use carbon emissions systematically and exploring their influencing factors for the rational allocation of land resources is of great importance and promoting collaborative emission reduction in this region. Based on the calculation of land use carbon emissions in Ningxia and its prefecture-level cities from 2000 to 2021, the regional differences in carbon emissions, economic efficiency, and carbon sink capacity were reflected through the difference index, carbon emission intensity, economic contribution rate, and carbon sink ecological carrying capacity. The results were as follows： ① From 2000 to 2021, the land use carbon emissions in Ningxia showed a significant increase by 110 919 400 t. Construction land was the main carbon source land, accounting for 99.57% of the total carbon emissions in 2021, and forest land was the main type of carbon absorption, accounting for 79.22% of the total carbon absorption in 2021. ② During the research period, the carbon emission difference among prefecture-level cities showed a trend of first rising and then slightly falling, with the gap reaching the maximum in 2016. ③ Although the overall difference in carbon emission intensity among prefecture-level cities showed a trend of narrowing and convergence, the economic contribution coefficient and carbon sink ecological carrying coefficient had significant differences, and the economic contribution rate and carbon emission contribution rate were both in a relatively unbalanced state, with obvious regional differences. ④ Land use carbon emission intensity, land use structure, economic development level, and population all played a promoting role in land use carbon emission, with contribution rates of 56.48%, 41.27%, 85.20%, and 9.29%, respectively. The contribution value of land use carbon intensity per unit GDP was negative, which inhibited the increase of land use carbon emission.

The Yellow River Basin assumes an important ecological and economic function in China. The study of carbon emissions from land use in the nine provinces (regions) of the pathway is important to achieve carbon reduction. Based on the dynamic data of land use, energy, and economic changes in nine provinces (regions) for the past 30 years from 1990 to 2018, this study analyzed the spatial and temporal evolution characteristics of land-use carbon emissions by using the carbon emission coefficient method in the IPCC inventory method and evaluating the low-carbon development model of the nine provinces (regions) by land-use carbon emission intensity. Finally, the LMDI model was used to analyze the factors influencing land-use carbon emissions. The results showed that: (1) in the past 30 years, the net carbon emissions have shown a continuously increasing trend, and the difference in the spatial distribution of carbon emissions in different periods was obvious. The carbon sink effect was not significant enough to offset the carbon emissions generated. (2) The continuously decreasing carbon emission intensity values per unit of GDP indicate that the coordination between land-use and economic development was getting better. (3) The factors of population size, economic size, and land-use structure accelerated land-use carbon emissions, whereas land-use efficiency limited land-use carbon emissions. Accordingly, this paper puts forward some corresponding policy suggestions.

Land use and cover change (LUCC) is a major driver of this rapid increase in atmospheric carbon. To reasonably plan various types of land use areas and mitigate the rate of growth of carbon emissions, this study takes Yaan, Sichuan province, as a case study. First, the calculation of Yaan's land use carbon emissions (LUCE) was approached by taking land use structure into account. Subsequently, the spatiotemporal distribution of LUCE was evaluated by employing the carbon emission risk index and the Moran index. Finally, the multi-objective linear programming (MOP), the Markov chain, and the PLUS model were used to predict the spatial distribution of LUCC in 2030, including natural development scenarios (NDS) and low-carbon optimization development scenarios (LODS). According to the findings, the impervious surface is identified as the principal contributor to LUCE, while the forest is recognized as the principal absorbers of carbon. The carbon emissions in typical mountainous areas are distributed in cities and generally concentrate towards the plains in the northeast direction. Under the LODS, LUCE decrease significantly. For both NDS and LODS, the overall trend of land development direction in Yaan is “northeast-southwest” from 2020 to 2030. These results could provide some suggestions for low-carbon land use in cities like Yaan.

As an important ecological civilization pilot zone and free trade port in China， Hainan Province undertakes the important task of coordinated development of carbon reduction and economic development under the background of the implementation of the strategy of "carbon peak and carbon neutrality." Based on the calculation of carbon source， carbon sink， and net carbon emissions in Hainan Province from 2004 to 2023， the LMDI model and Lasso analysis were used to decompose and screen the influencing factors of carbon emissions in Hainan Province， and four Lasso-Transformer neural network models were included to predict carbon emissions in Hainan Province from 2024 to 2030. The results showed that： ① The trend of total carbon sink in Hainan Province from 2004 to 2023 was relatively stable， and the change trend of net carbon emission was basically consistent with the total carbon source. ② The main influencing factors of carbon emissions in Hainan Province were energy intensity， land carbon emission intensity， economic efficiency， land use structure， population size， and land use efficiency. ③ Through model optimization， the Lasso-PatchTST model was used to predict the carbon emission of Hainan Province from 2024 to 2030 and its influencing factors， and the carbon emission in 2030 was predicted to be 43，455，300 tons. The growth rate of land use efficiency factor was the fastest， and the growth rate of population size was the slowest. By optimizing industrial structure， improving resource utilization efficiency and strengthening ecosystem protection， it can promote the coordinated development of carbon reduction and economy in Hainan Province. The results of this study can provide a reference for decision-making of low-carbon economic development in Hainan Province.

Carbon source and sink monitoring is an important prerequisite for realizing the dual-carbon target and the evolution of its spatial-temporal pattern and the spatial-temporal heterogeneity of the driving factors are the scientific basis for the implementation of the emission reduction and sink enhancement policy according to the local conditions, which is of great importance for the sustainable development of the region. Based on the carbon balance of payment relationship, the carbon surplus and deficit of Shaanxi Province counties were calculated in 2000, 2010, and 2020 from land use, and a series of exploratory spatial and temporal analysis methods （ESTDA）, including spatial autocorrelation, cold and hot spot analysis, standard deviation ellipse, and LISA-time pathway, were used to study the dynamics of carbon surplus and deficit in Shaanxi Province at different spatial-temporal scales. From the 21 indicators, six types of major driving factors were selected by principal component analysis, and the geographical spatio-temporal weighted regression model （GTWR） was used to identify their spatio-temporal heterogeneity to construct a comprehensive system of indicators to analyze the carbon deficit and its spatio-temporal heterogeneity in Shaanxi Province. The results showed that： ① A carbon surplus of 8.56 million tons in 2000, a carbon deficit of 3 296 tons in 2010, and a deficit of 33.34 million tons occurred in 2020 in Shaanxi Province, and the growth rate of carbon emissions was much larger than that of carbon sinks, which indicates that it was gradually moving towards carbon peaks in Shaanxi Province； however, there is a long way to go to achieve the goal of carbon neutrality. ② The geographical distribution of the "north deficit and south surplus" phenomenon was visually represented. A carbon deficit was concentrated in the wind and sand area along the Great Wall and Guanzhong plain. The spatial-temporal leap characteristics were more stable. In conclusion, efforts aimed at emission reduction and carbon sink enhancement were strategically directed towards the northern Shaanxi Region. ③ Among various indicator systems including urban construction, natural resources, anthropogenic activities, energy consumption, industrial development, and ecological protection indicator systems, only ecological protection positively drove carbon profit and deficit. Notably, natural resources had the strongest spatial and temporal heterogeneity in their impact on carbon deficit, and energy consumption was positively driven in some areas of Shaanxi Province. The results will provide accurate policy directions for the development of carbon neutral strategies in Shaanxi Province.

Land use carbon emissions (LUCE) contribute significantly to global warming. Recognizing the influence of regional heterogeneity and geographical scale on socioeconomic development, studying LUCE at various scales is crucial for devising more effective emission reduction measures. However, previous studies have predominantly focused on a single scale. This study focuses on the Yangtze River Economic Belt (YREB), utilizing land use, nighttime light, and energy consumption data to compute LUCE at provincial, prefectural, and county scales, employing spatial autocorrelation, geographic detectors, and the Multiscale Geographically Weighted Regression (MGWR) model to analyze the spatiotemporal dynamics and impact factors of LUCE across different scales. Our results show: (1) Throughout the study period, LUCE in the YREB exhibited a steady increase, rising from 28,434.32 × 104 t to 86,581.79 × 104 t. (2) Positive spatial autocorrelation was observed in LUCE at all three scales. Notably, spatial clustering intensified at the provincial and prefectural levels, while a diminishing trend in clustering was noted at the county scale. (3) Predominant clustering patterns at the prefectural and county scales included H–H and L–L types, with the county scale displaying more pronounced clustering characteristics. (4) Economic development emerged as the primary influencing factor on LUCE at both the prefectural and county scales. Nevertheless, the intensity of impact from carbon emission intensity, industrial structure, population size, government intervention, and land use degree differs between the two levels. This research underscores the high sensitivity of LUCE to administrative scales, emphasizing the necessity of considering these scales when formulating emission reduction strategies.

Compared to general public and residential buildings, large public buildings are often difficult to construct and have a long construction period, creating greater construction energy consumption and carbon emissions on the one hand, while generating a large amount and many types of difficult-to-track process data on the other. As such, it is difficult to measure carbon emissions and analyze various influencing factors. By realizing the simple calculation of energy consumption and carbon emissions, as well as discerning the degree of influence of various factors based on the results of influencing factors research, it is of considerable practical significance to propose energy savings and emission reductions in a targeted manner. In view of the above, this work aimed to establish a more practical calculation method to measure energy consumption and carbon emissions in the construction of large public buildings, as well as to identify the multiple influencing factors related to energy consumption and carbon emissions during the construction process. To demonstrate the practicality of our approach, quantitative calculations are carried out for a new terminal building in a certain place and from the perspective of sustainable urban construction; thus, the driving factors of the traditional STIRPAT model are extended to seven. Based on the calculation results, a modified STIRPAT model is used to analyze the comparative study of impact factors, such as population and construction machinery performance, on energy consumption and carbon emission intensity. The results show the following: (1) The energy consumption value per square meter of this terminal building is 3.43 kgce/m2, and the average carbon emission per square meter is about 13.88 kgCO2/m2, which is much larger than the national average of 6.96 kgCO2/m2, and (2) the type of energy used in the construction process has the greatest degree of influence on energy consumption and carbon emission, and the local GDP, population factor, construction machinery performance specifications, and shift usage also show a positive correlation with the growth of total energy consumption and carbon emissions. Moreover, while the government’s continuous investment in energy conservation and environmental protection has reduced the total energy consumption and carbon emissions in construction, there is still considerable room for improvement. Finally, according to the results, we provide theoretical references and constructive suggestions for the low-carbon construction of large public buildings in the construction stage. Thus, the results of our study will allow policy makers to formulate appropriate policies.

In the process of urban renewal, the high carbon emissions caused by pollution from construction waste and the consumption of materials and energy via “demolition and construction” present significant problems. Calculating carbon emissions and sinks is a prerequisite to carving out a low-carbon path via urban renewal. In this paper, we take an environmental improvement project in the Suzhou Industrial Investment Science and Technology Park as an example. Under the framework of the entire life cycle, we define the “renewal phase”, which includes “demolition” and “construction”, as the calculation boundary, and use the emission coefficient method as the primary calculation approach to assess carbon emissions and sinks. We construct a design-oriented carbon revenue and expenditure estimation system based on the BIM model. After its application in empirical cases, the results show the following: ① The building carbon emissions of Scheme A are 342 t higher than those of Scheme B, due to the fact that the “demolition and construction” works in the former increased by 6000 m2 compared to the latter. ② The landscape carbon emissions of Scheme B are 269 t higher than those of Scheme A, due to the addition of a 2500 m2 reinforced concrete overhead walking platform. ③ The annual carbon sink of Scheme A is six times higher than that of Scheme B, due to the fact that the number of trees and shrubs in the former is five-to-six times greater than in the latter. The number of trees planted plays a decisive role in enhancing the carbon sink benefit. ④ In the year during which the renewal project was implemented, the difference in carbon emissions between Scheme A and Scheme B was 72.9 t. By the 16th year, the difference in carbon emissions between the two schemes approached zero, and the carbon reduction advantages of Scheme A became more pronounced over the entire life cycle thereafter. Finally, this article summarizes four low-carbon design strategies for industrial park renewal projects: rational construction and demolition, three-dimensional parking, low-carbon materials, and plant carbon sequestration. In the context of inventory renewal, this article makes a significant contribution in terms of carbon footprint control and the “dual-carbon” goal.

基于生命周期的风电场碳排放核算

Comprehensive assessment of land use carbon emissions of a coal resource-based city, China

Given the escalating challenges posed by global climate change, as the world's largest carbon emitter, China is facing a huge challenge in achieving its "dual carbon" goals. Therefore, reasonable prediction of China's carbon emission intensity is crucial for formulating effective emission reduction strategies. Considering the external shocks faced by the economic system, the time breakpoint is introduced into the traditional grey prediction model. The model is optimized from two aspects： accumulation method and background value, and a new grey breakpoint model with inverse accumulation is constructed. Based on the calculation of China's carbon emissions, the carbon emission intensity from 2023 to 2030 was predicted. The following conclusions were drawn： ① By adding time breakpoints, the new model achieved accurate prediction of the future trend of the system under external shocks, further reflecting the principle of information priority in the modeling process. ② Under the external impact of the COVID-19, the growth rate of China's GDP further slowed down, and the carbon emissions showed different characteristics in the four regions. The carbon emissions in the northeast began to decline gradually, while the carbon emissions in the eastern and western regions accelerated. ③ From 2023 to 2030, China's carbon emission intensity will considerably decrease. Compared with that in 2020, the carbon emission intensity is expected to decrease by 13.2% in 2025 and by 22.6% in 2030, with the highest decline in the northeast and the lowest in the east. However, under current conditions, China still finds it difficult to fully achieve its 2025 and 2030 emission reduction targets, with the eastern and western regions facing enormous pressure to reduce carbon emissions.

Changes to land use carbon emissions (LUCEs) have become significant contributors to increasingly severe climate issues. Land use change is one of the crucial factors that affect carbon emissions. Alpine meadows regions are sensitive to climate change and human activities. However, current research on LUCEs mainly focuses on analyzing present land use status and spatial patterns. To reveal and forecast future LUCEs in the alpine region, the Upper Yellow River of Gannan (UYRG) was used as a case study. Based on the land use data from 1990 to 2020, we used the multi-scenario PLUS model to predict the land use types in 2030 and analyzed the spatial and temporal dynamic trends of LUCEs from 1990 to 2030. The results showed a strong correlation between the predicted and actual land use types, with a Kappa value of 0.93, indicating the applicability of the PLUS model in predicting land use in the UYRG. Over the study period, construction land expanded, while woodland and grassland diminished. Carbon emissions (CEs) increased by 516.4% from −200,541.43 Mg CO2e in 1990 to 835,054.08 Mg CO2e in 2020, with construction land being the main contributor. In the Natural Development scenario for 2030, construction land expanded most rapidly, resulting in the highest LUCEs. In the Ecological Protection scenario, woodland and grassland expanded, while construction land decreased, leading to an expansion in carbon sinks. In the Cropland Protection scenario, cropland expanded, with CEs falling between the other two scenarios. These findings lay a theoretical groundwork for formulating policies addressing LUCEs in alpine meadows, providing valuable insights for further studies.

Land use changes significantly impact both carbon emissions and ecosystem service value (ESV). However, few studies have been conducted on the spatial relationship between land use carbon emissions (LUCE) and ESV. Thus, focused on the Yellow River Basin (YRB), this study independently calculates carbon emissions from land use change (LUCE) and ecosystem service values (ESV) in the region. Utilizing spatial autocorrelation methods, we analyze the spatiotemporal pattern of LUCE and ESV and subsequently apply the bivariate spatial autocorrelation method to explore their spatial relationship. The results prove that: (1) The YRB's LUCE has continuously increased, with construction land acting as the dominant carbon source and woodland acting as the main carbon sink. The LUCE in the YRB had a positive spatial autocorrelation. (2) The YRB's ESV increased. Spatially, the ESV in the YRB showed a positive autocorrelation. (3) Both LUCE and ESV exhibited negative spatial autocorrelation, with predominant patterns of bivariate localized spatial autocorrelation identified as High-Low agglomeration (H-L) and Low-High agglomeration (L-H). Cities with the L-H pattern were primarily located in Qinghai Province and Inner Mongolia. In contrast, cities with the H-L pattern were mainly observed in the western section of Shandong and the northeastern region of Henan. The study revealed the negative impact of increased carbon emissions from land use on the value of ecosystem services, providing assistance in the development of relevant environmental policies and promoting sustainable development in the YRB.

Spatial-temporal characteristics of carbon emissions corrected by socio-economic driving factors under land use changes in Sichuan Province, southwestern China

Optimizing land use and management are pivotal for mitigating land use-related carbon emissions. Current studies are less focused on the influence of development policies and spatial planning on carbon emissions from land use. This research employs the future land use simulation (FLUS) model to project land-use alterations under the business-as-usual (BAU) and low-carbon ecological security (LCES) scenarios. It assesses and predicts spatiotemporal characteristics of land-use carbon emissions in the Beijing-Tianjin-Hebei (BTH) region across urban agglomerations, cities, counties, and grids from 2000 to 2030. The influence of low-carbon policy is assessed by comparing the land-use carbon emissions between scenarios. The findings demonstrate that: (1) Urban agglomeration-wise, Beijing’s land-use carbon emissions and intensities peaked and declined, while Tianjin and Hebei’s continued to rise. (2) City-wise, central urban areas generally have higher carbon emissions intensities than non-central areas. (3) County-wise, in 2030, high carbon-intensity counties cluster near development axes. Still, the BAU scenario has a larger carbon emission intensity and a greater range of higher intensities. (4) Grid-wise, in 2030, the BAU scenario shows a clear substitution of heavy carbon emission zones for medium ones, and the LCES scenario shows a clear substitution of carbon sequestration zones for light carbon emission zones. Our methodology and findings can optimize spatial planning and carbon reduction policies in the BTH urban agglomeration and similar contexts.