Emission Reduction Strategies Research Articles

Industrial carbon emission efficiency prediction and carbon emission reduction strategies based on multi-objective particle swarm optimization-backpropagation: A perspective from regional clustering

Against the backdrop of global climate change, industrial carbon emission reduction has become an important pathway to for global low-carbon development. This study constructs a framework of geographic spatial constraints regionalization and multi-objective machine learning to predict future industrial carbon emission efficiency (ICEE) and explore strategies for carbon emission reduction. Firstly, the ICEE of 285 Chinese cities were calculated by the super-efficiency slacks-based measure. Secondly, the cities were classified into four ICEE level regions through the spatially constrained multivariate clustering. Next, the multi-objective particle swarm optimization-BP (MOPSO-BP) model was constructed to predict the future trends of ICEE in the four regions. Finally, the geographical detector and multi-scale geographically weighted regression were employed for exploring driving force and carbon emission reduction strategies in different regions. The results show that most cities had low or medium ICEE, while super efficiency cities were mainly distributed in the east coastal areas. The prediction performance of the MOPSO-BP model for the four regions was better than the ordinary particle swarm optimization-BP and traditional BP model. Except for the Agricultural Production Region, there is considerable room for improving the ICEE of other regions over the next decade. Macroeconomic and microeconomic development have a global effect in promoting regional ICEE improvement, urban construction shows a promoting or inhibiting effect in different regions, and information technology has significant spatial heterogeneity in its influence within each region. The analysis framework developed in the study is a reliable solution for managing and planning ICEE and provides constructive suggestions for future regional low-carbon development.

Fuel consumption and carbon emission reduction strategies in a fuel vehicle supply chain under auto double credit policy and carbon social responsibility

Fuel consumption and carbon emission reduction strategies in a fuel vehicle supply chain under auto double credit policy and carbon social responsibility

Developing basin-specific emission factors for accurate methane inventory and emission reduction strategies in Zonguldak Coal Basin, Turkey: Implications for enhancing safety and sustainability

This study develops basin-specific emission factors using historical coal production and methane emissions data to enhance methane inventories and emission reduction strategies in Turkey's Zonguldak Coal Basin. The basin is known for high levels of coal mine methane (CMM) emissions, posing a serious safety hazard to miners and affecting mine productivity. However, limited data on in-situ parameters related to Coal Mine Methane (CMM) emissions have been available. The study analyzes historical trends in CMM emissions (1980–2020) from the basin's underground coal mines, revealing declining emissions alongside coal production. Over this period, approximately 2.28 billion cubic meters of CMM were released. Continued historical coal production rates suggest a significant 6.63 MWe power generation potential. The investigation into coal production, average mining depth, and coal seam gas content reveal that CMM emissions primarily depend on coal production and mining depth. Utilizing available methane mitigation technologies could prevent an estimated 55.7 million tons of methane annually (equivalent to around 692,000 tons of CO2e). Importantly, the study calculates basin-specific emission factors (ranging from 7.24 m3/t to 21.66 m3/t) based on historical measurements, providing a more accurate regional representation than the Intergovernmental Panel on Climate Change (IPCC) default values. These factors are vital for identifying high-emission areas and guiding emission reduction strategy development. This study addresses a critical data gap in Turkey's estimation of methane emissions from underground coal mining, particularly in the Zonguldak coal basin. It establishes a foundation for future research on methane emissions from coal mines at various scales, contributing to improved understanding, emissions mitigation, safety enhancement, and sustainable coal mining practices.

Integration of in-situ and ex-situ power-to-gas (PtG) strategy for simultaneous bio-natural gas production and CO2 emission reduction

A novel system integrating an in-situ and ex-situ power-to-gas (PtG) system was developed in the current study. A continuous stirred-tank reactor (CSTR) was operated using cattle manure as substrate at mesophilic temperature (37 °C ± 2 °C). The CH4 content in the biogas was upgraded to above 95% by H2 injection, which meets the highest criteria for grid injection without requiring CO2 removal. Furthermore, the bio-nature gas production was promoted by external CO2 and H2 injection. The volumetric methane production rate (VMPR) was significantly increased by 739% from 117.4 mL L−1·d−1 to 985 mL⋅L−1⋅d−1, which is higher than in other studies. Meanwhile, the volumetric biogas production rate (VBPR) was increased by 36.9% by H2 injection, increasing the conversion efficiency (82.56%) of the chemical oxygen demand (COD) to CH4. A significant increase in the specific methanogenic activity of dissolved hydrogen (SMA(Hdissolved)) and the enrichment in hydrogenotrophic methanogens (Methanobacterium) demonstrate that the CH4 production pathway was converted from acetoclastic methanogenesis (AM) pathway to hydrogenotrophic methanogenesis (HM) pathway. It is postulated that the change in proportion of different pathways of the CH4 production was caused by the strengthening of key enzymes (coenzyme F420 hydrogenase and coenzyme-B sulfoethylthiotransferase) by H2 injection. The integrated system represents a promising approach to achieve simultaneous CO2 emission reduction and bio-natural gas production.

Novel organic-inorganic hybrid towards enhancement of flame retardancy, suppression of volatile organic compounds and toxic smokes for asphalt

The enhancement of flame retardancy, the suppression of volatile organic compounds (VOCs) and carcinogenic smokes at elevated temperature are urgent for the widely application of asphalt. In present work, an organic-inorganic hybrid flame retardant (ATH@SA@Fe3+) was designed via simple two-step reactions, including in-situ growth of sodium alginate (SA) on the surface of alumina trihydrate (ATH) and a crosslinking of iron ion (Fe3+) on ATH@SA. The three-dimensional network crosslinking structure of ATH@SA@Fe3+ effectively hindered and retarded the thermal decomposition progress of asphalt, producing fewer hydrocarbon derivatives, reducing VOCs constituents and their release amounts. For the Asphalt/10ATH@SA@Fe3+ composites, the total heat release (THR), the total smoke production (TSP) and the peak smoke production rate (pSPR) was reduced by 28.2%, 35.2% and 28.0% respectively, indicating a high flame-retarding and smoke suppression efficiency via the formation of expanded, impact and continuous carbon layer. Meanwhile, the network crosslinking structure could arrest the molecular motion and lead to the thermal stability, elasticity rutting resistance enhancement of asphalt materials. In perspective, the research results provided a scientific basis for the management of flame-retardant materials for asphalt pavement and the formulation of emission reduction strategies.

Optimal vehicle fleet planning and collaboration under carbon neutrality: a game-theoretic perspective

This paper studies the optimal vehicle fleet planning and collaboration problem for a fuel vehicle (FV) transport service provider, a commercial electric vehicle (CEV) transport service provider, and a carbon emission treatment agency under carbon neutrality. The FV transport service provider pays a fixed fee or a portion of its sales revenue to a carbon emission treatment agency in exchange for technology to reduce its carbon emissions, and it can adopt three strategies (i.e., no emission reduction, purchasing technology for emission reduction, and entrusting a carbon emission treatment agency). We derive each party’s optimal fleet size, price, and profit in the three scenarios. Our results suggest that carbon emission reduction strategies may improve the market performance of the FV transport service provider. Then, we find no certain strategy is always preferable to another: the optimal cooperation strategy between the transport service provider and carbon emission treatment agency depends on the fixed technology fee, ratio of revenue sharing, government penalty, the transport service market potential, and consumer green preference, as well as the cost per CEV. This paper gives the transport service provider and carbon emission treatment agency a full picture of whether, when, and how to collaborate in green commerce.

Spatiotemporal variations of atmospheric nitrogen deposition in China during 2008–2020

In recent decades, China has been a hotspot for reactive nitrogen (N) deposition due to intensive fossil fuel burning and increased agricultural activities. The Chinese government has implemented active measures to protect air quality and reduce N deposition. In this study, we combined monthly measurements of monitoring sites and satellite data to construct random forest (RF) models for the estimation of monthly N deposition in China during 2008–2020. This provides N deposition estimation in latest years and has a finer temporal resolution. The RF models can address more complicated relationships between variables and performed well in estimating N deposition compared to the measurements with average correlation coefficients of 0.79 and 0.77 for dry and wet N deposition, respectively. We found that mean annual total N deposition was 22.0 ± 0.8 kg N ha−1 yr−1 during 2008–2020, which accounted for 64% (±4%) of reactive N emissions in China. Although the total N deposition was high, it has stabilized and decreased slightly over the past decade, with dry and wet N deposition contributing equally to the total N deposition. NHx deposition (FNHx) still slowly increased (0.09 kg N ha−1 yr−2) during 2015–2020, when N fertilizer application and livestock started to decrease. This is due to the rapid decline in NOx and SO2 emissions during the same period. Our results provide a new perspective on the spatiotemporal variations in the total N deposition in China. It is necessary to optimize other emission reduction strategies to mitigate air pollution and N deposition.

Optimizing the use of forestry biomass producer gas in dual fuel engines: A novel emissions reduction strategy for a micro-CHP system

Low energy density fuels, like producer gas from biomass gasification, can be profitably used in compression ignition (CI) engines by means of a proper combustion strategy, such as the dual-fuel mode. Experimental investigations have proven the environmental benefits of the use of producer gas in terms of green-house gas emissions but critical issues related to their use in CI engines have been also highlighted. In particular, under specific operating conditions, the performance and the emissions of the engine might be derated reducing the attractiveness of the technology. In this work a new data-driven emission optimization strategy for a micro-cogeneration system based on an open-top biomass gasification unit coupled with a CI engine running in dual-fuel mode (producer gas/diesel) is presented and discussed. The main purpose of the work is to provide an appropriate tool to address one of the typical problems of dual-fuel systems, namely the nitrogen oxides (NOx) and carbon monoxide (CO) emission trade-off, by calculating the optimal diesel substitution rate (DSR) at a certain required power load. The methodology baseline is the experimental characterization and parametric modeling of the system in terms of NOx, CO, and electrical efficiency (ηe). Then a trade-off objective function (TOF) is defined. The novelties of the proposed approach stands in the building of an ad-hoc designed TOF so as to describe the problem with a single-objective optimization, and thus avoiding the computational and time complexity of a multi-objective optimization. Moreover, a second peculiar aspect is the introduction in the TOF of an internally dependent coefficient (namely the load factor Kl) able to take into account the effect of load and DSR on CO emissions: the adding of such a physical-related feature represents an effective enrichment of the minimization strategy, especially considering the nature of the optimization, which is purely data-driven. Finally, sequential quadratic programming (SQP) minimization of the TOF is carried out by means of the available MATLAB SQP libraries, and two optimization strategies – namely, CO-driven and efficiency-driven - are discussed. Results show (i) promising potentialities in identifying the best effective utilization range of dual-fuel combustion; (ii) high flexibility in the use of the optimization algorithm thanks to the definition of the TOF, which can be easily adapted to different objectives (e.g., NOx mitigation, maintenance of high electrical efficiencies, CO emission bounding) while maintaining external constraints; (iii) high robustness even in the extreme cases in which the imposition of a particular constraint (e.g., high electrical efficiency) is not compatible with the capabilities of the system.

How does public concern about climate change affect carbon emissions? Evidence from large-scale online content and provincial-level data in China

Micro-level concerns about climate change can not only affect individual low-carbon behaviors but also exert intense pressure on carbon emission reduction strategies and policy design. However, whether public concern about climate change can affect per capita carbon emission is still unclear. Based on panel data from 30 provinces in China and 188,943 climate change-related blogs from 2012 to 2019, this research investigates the impact of public concern on per capita carbon emissions. Results show that, in general, public concern about climate change has a significant negative impact on per capita carbon emissions in China, indicating that climate crisis awareness has a supervisory effect on greenhouse gas emissions. Nevertheless, the effects of different public concerns on per capita carbon emissions are different, with the topics of “low-carbon appeal” and “concern for future” have significant positive impacts on per capita carbon emissions, while the topics of “carbon governance performance” and “climate change mitigation strategies” have significant negative impacts on per capita carbon emissions. The findings yield new opportunities to delve into the relationship between the externality of people's will and governmental behaviors and provide in-depth insights for decision-makers to develop tailor-made solutions towards carbon reduction and sustainable development.

A Study on the Drivers of Carbon Emissions in China’s Power Industry Based on an Improved PDA Method

The power industry is a major source of carbon emissions in China. In order to better explore the driving factors of carbon emissions in China’s power industry and assist the Chinese government in formulating emission reduction strategies for the power industry, this study applies the improved production-theoretical decomposition analysis (PDA) method to analyze the carbon emission drivers of China’s power industry. This study investigates the impact of energy intensity, per capita GDP, population density, power generation structure, and environmental climate on carbon emissions in China’s power industry in 30 provinces from 2005 to 2020. It was found that the carbon emission ratios of the power sector in all provinces and cities are basically greater than 1, which indicates that carbon emissions in most of the power sectors in the country are still increasing as of 2020. Overall, the effects of potential thermal fuel carbon emission efficiency, potential thermal energy consumption efficiency, the carbon emission efficiency of thermal power generation, economic scale, population density, and annual rainfall change are mostly greater than 1 and will promote the growth of carbon emissions in the power sector. Moreover, the effects of thermal power generation energy efficiency technology, thermal power generation emission reduction technology, power generation structure, and power generation per unit GDP are mostly less than 1 and will inhibit the growth of carbon emissions in the power sector. However, each of these drivers does not have the same degree of influence and impact effect for each province and city. Based on the research results, some policy recommendations are proposed.

Analysis of Influencing Factors of Rural Residential Carbon Emissions Under the Background of Dual Carbon Target

This paper studies the carbon emission of rural residential buildings under the background of ' double carbon target '. Based on the characteristics of rural residential buildings and the current situation of carbon emission, the literature research method is used to identify the residential characteristics, enclosure structure, family situation, lifestyle, energy consumption equipment, renewable energy utilization and agricultural production function. The carbon emission influencing factors of 21 rural residential buildings in the use stage of rural residential buildings are helpful to evaluate the carbon emission of buildings more accurately, provide the basis for formulating carbon emission reduction strategies, and promote the sustainable development of rural residential buildings. It is of great significance.

Carbon Emissions Assessment for Building Decoration Based on Life Cycle Assessment: A Case Study of Office Buildings

The continuous growth of interior decoration activities has caused a massive consumption of energy and materials, which has contributed to a large amount of carbon emissions in the construction sector. The carbon emissions of building decoration were overlooked in previous studies. Hence, the life cycle assessment (LCA) approach was employed to build a life cycle carbon emissions model for building decoration. An office building was selected to verify the availability. The results show that the carbon emissions intensity of the decoration project was 254.5 kg CO2 eq/m2. The operation stage was the most crucial carbon emissions contributor in the life cycle of building decoration, accounting for 49.8%; followed by the materials embodied impact stage, which contributed 36.3%; while the remaining three stages, namely, the decoration, transportation, and end-of-life stage, had less carbon emissions, accounting for 6.8%, 5.3%, and 1.8%. Improving the performance of inorganic materials, optimizing transportation routes and energy structure, and dismantling plan optimization can reduce carbon emissions. The findings of this study provide a theoretical basis and fundamental data for carbon emissions reduction and sustainable development strategies for building decoration.

Carbon emissions abatement with duopoly generators and eco-conscious consumers: Carbon tax vs carbon allowance

With electrification and economic development, global carbon emissions are rising every year. Among them, the power industry accounted for the highest proportion of carbon emissions. To combat climate change, environmental pollution and economic losses caused by greenhouse effect, governments implement low-carbon policies in the electricity market. Considering the impact of generators’ carbon emissions on consumers behaviors and environmental benefit, this paper compares two main emission reduction policies — carbon tax and carbon allowance in a market with two asymmetric generators and a group of heterogeneous eco-conscious consumers. Despite the widespread belief that environmental advocacy hurts economic performance and social welfare, we find that both policies improve social welfare due to price increases or production restrictions. And when neither generator has a significant cost advantage, the social welfare under carbon allowance is higher than that under carbon tax, and the total carbon emissions are lower. This is because the carbon tax indirectly regulates generators’ output and total welfare by affecting prices. While carbon allowance can directly regulate generators’ output through capacity limits, and further improve the total social welfare. Through numerical analysis, the optimal emission reduction strategies of the government under different conditions are determined. We also extend the main model and show that the key results are still valid.

The impact of carbon policy on corporate risk-taking with a double/debiased machine learning based difference-in-differences approach

This study adopts the more cutting-edge DMLDID (double/debiased machine learning based difference-in-differences) approach to demonstrate the impact of carbon policy on corporate risk-taking, and the strong conclusion suggests that carbon policy significantly reduces corporate risk-taking. The further analysis concludes that carbon policy negatively impacts corporate risk-taking by reducing investor attention and raising financing constraints. Also carbon policy significantly reduces the risk-taking of SOEs and heavy polluters. This study has been shown to have great significance for firms to resist external policy risks, mitigate internal business risks, develop emission reduction strategies, and achieve sustainable corporate development.

Spatial Characteristics and Influencing Factors of Multi-Scale Urban Living Space (ULS) Carbon Emissions in Tianjin, China

Urban living space (ULS) is known to be a significant contributor to carbon emissions. However, there is a lack of studies that have considered the impact of spatial organization indexes (SOIs) of various scales on urban living space carbon emissions (ULSCE), and so far, no definitive conclusions have been reached. To address this gap, taking Tianjin as an example, the measurement methods of ULSCE and SOI at different scales were proposed, and a random forest model was constructed to explore the effects of SOI on ULSCE. The results indicated that on the district scale, Beichen had the highest carbon emissions and absorption in 2021, with carbon emissions reaching 1.43 × 108 t and carbon absorption at 7.29 × 105 kg. In terms of area scale, the comprehensive service area had the highest carbon emissions at 3.57 × 108 t, accounting for 47.70%, while the green leisure area had the highest carbon absorption at 5.76 × 105 kg, accounting for 32.33%. At the block scale, the industrial block had the highest carbon emissions at 1.82 × 108 t, accounting for 54.02%, while the forest block had the highest carbon absorption at 1.25 × 106 kg, accounting for 91.33%. Each SOI had varying impacts, with the industrial land ratio (ILR) having the highest order of importance at the area scale, followed by road network density (RND), residential land ratio (RLR), bus station density (BSD), public service facilities land ratio (PLR), land mixing degree (LMD), open space ratio (OSR), and commercial land ratio (CLR). ILR, RND, and RLR were particularly important, each exceeding 10%, with importance values of 50.66%, 17.79%, and 13.17%, respectively. At the block scale, building area (BA) had the highest importance, followed by building density (BD), building height (BH), land area (LA), and floor area ratio (FAR). BA and BD were particularly important, with values of 27.31% and 21.73%, respectively. This study could serve as both theoretical and practical guidance for urban planning to aid the government in developing differentiated carbon emissions reduction strategies that can mitigate the heat island effect and promote low-carbon healthy urban planning.

Spatio-temporal analysis of carbon footprints for urban public transport systems based on smart card data

The increasing severity of global climate change has made reductions in carbon emissions an urgent global issue. The relative lack of carbon footprint analyses of urban public transportation systems (UPTS) is therefore surprising, given that UPTS is an important component of urban transportation and one that may play a crucial role in carbon emission reduction. This study conducts a spatio-temporal analysis of carbon footprints for UPTS during the COVID-19 pandemic based on smart card data in Beijing. Since the core of carbon footprint calculation is to estimate travellers' trip trajectories and the ridership of urban rail transit (URT) and buses, we construct a novel multi-layer urban rail network model to calculate passenger volume and travellers' trajectories through a traffic assignment model. Furthermore, we utilize the Generalized Additive Model (GAM) to analyse the correlation relationship between the carbon footprints of buses and URT. Additionally, we conduct statistical analysis of the carbon footprint of UPTS. The results of the spatio-temporal analysis of carbon footprints for UPTS show significantly lower carbon emissions during holidays compared to those on working days, and emissions during peak hours contribute approximately half of the total daily UPTS emissions, while there are notable variations in the distribution of the carbon footprint among different districts. Moreover, our analysis reveals a positive correlation between the carbon footprints of buses and URT. The statistical analysis reflects different patterns of carbon footprint distribution on different dates during the pandemic, but the carbon footprint distributions on selected dates all follow a power-law distribution. This study may facilitate the understanding to the impacts of UPTS on the environment during the COVID-19 pandemic, and also provide important guidance and reference for the development of carbon emission reduction strategies.

Measurement report: Source apportionment and environmental impacts of volatile organic compounds (VOCs) in Lhasa, a highland city in China

Abstract. Hypoxia and adverse health outcomes might be affected by O3 pollution in the highland city of Lhasa. NOx emissions can amplify the role of volatile organic compounds (VOCs) in the secondary production of O3 under the conditions of high ultraviolet (UV) radiation levels and unfavourable dispersion patterns in the Lhasa River valley. Here, online C2–C11 VOC measurements, accompanied by other parameters concerning the O3 chemical budget, were first obtained and employed to identify the key VOC species and key sources of VOCs in terms of the loss rate against OH radicals (LOH), ozone formation potential (OFP), secondary organic aerosol potential (SOAP), and toxicity. Oxygenated VOCs (OVOCs) not only were the most abundant VOCs but also dominated LOH, OFP, and toxicity. Isoprene and anthropogenic VOCs were further identified as precursors of these OVOCs. Aromatics accounted for 5 % of the total VOCs (TVOCs) but contributed 88 % to the SOAP and 10 % to the toxicity. As the primary oxidative intermediates of aromatics were not well characterized by our measurements, the environmental impact of aromatics could be underestimated by our data. Source appointment and ternary analysis of benzene, toluene, and ethylbenzene confirmed the combined contribution of traffic emissions, solvent usage, and biomass burning. This suggests that O3 precursors are mainly from sources associated with residents' lives, except for solvent usage emissions which contribute to aromatics. Preliminary comparisons between source spectrums of transport sector emissions with positive matrix factorization (PMF)-decomposed ones and our measured ones suggest that vehicle emission patterns of VOCs at high altitude generally follow the ground-level impression. More quantitative data are required to further confirm this point though. Emission reduction strategy analysis for O3 pollution control highlighted multiple benefits of the simultaneous reduction in NOx originating from diesel vehicle emissions, biomass burning, and background sources (possibly dominated by incense burning). The notable biogenic emission contribution to the OFP was also first confirmed in our study, and this highlights the side effects of the government's pursuit of a greener city.

Coupling analysis and driving factors between carbon emission intensity and high-quality economic development: Evidence from the Yellow River Basin, China

This study aims to explore the complex dynamic relationship between carbon emission intensity (CEI) and high-quality economic development (HQED) in the Yellow River Basin (YRB), with the goal of fostering ecological protection and high-quality development strategies. However, there is limited knowledge on the comprehensive development level, evolutionary trends, and driving factors of these two systems in the YRB. To address this, the study uses multiple methodologies, including nighttime light data, the entropy-topsis method, the coupling coordination degree model (CCDM), exploratory spatial data analysis (ESDA), and the geodetector model, to examine the coupling coordination relationship and driving factors between CEI and HQED across sixty cities in the YRB from 2010 to 2019. The results reveal a decreasing trend in CEI and a “U”-shaped development trend for HQED, characterized by low coordination and significant spatial imbalance. Moreover, per capita GDP, population density, urbanization level, industrial structure, and energy intensity are found to have significant driving effects on the CCD. Based on the results, the study suggests policies to boost the CCD between CEI and HQED. These involve promoting green transitions, endorsing spatially tailored development strategies, and implementing interventions on major CCD drivers for balanced growth. It also proposes refining regional governance and establishing an innovation fund for green technologies, particularly beneficial for underdeveloped regions. These policy implications could guide economic development and emission reduction strategies in ecologically fragile and economically underdeveloped regions.

An Integrated Approach to Analyze the Progress of Developing Economies in Asia toward the Sustainable Development Goals

This study endeavored to analyze the progress made to meet the Sustainable Development Goals in terms of resource use, sustainable production and consumption, and the triple planetary crisis (i.e., climate change, biodiversity loss, and pollutant emissions) at the national and regional levels in Asia. The study highlighted that the progress toward sustainable consumption and production is still not sufficient to meet the ambitious national targets. An urgent need for a comprehensive approach to address climate change, biodiversity loss, pollutant emissions, and resource use has been ascertained. China’s greenhouse gas emissions have surged tremendously. India is also endeavoring to decouple emissions from growth via renewable energy. Vulnerable Pakistan seeks emission reduction and financial aid. Indonesia, Thailand, and Vietnam outline emission reduction strategies. Land use change emerges as a key biodiversity loss driver, stressing the need for sustainable land policies and conservation. Material consumption highlights the call for production optimization, circular economies, and innovative technology. Energy’s role in development requires decoupling from growth through efficiency, renewables, and eco-friendly paths. Freshwater needs careful management for sustainability, and international collaboration and policy reform are urged for global water use efficiency. Decoupling trends between growth, resource use, and environmental impact show a complex pattern, with the feasibility of absolute decoupling limited by growth interdependence.

Decoupling Effect and Driving Factors of Land-Use Carbon Emissions in the Yellow River Basin Using Remote Sensing Data

Land-use change is a crucial element influencing the patterns of carbon sinks/sources in the Yellow River Basin (YRB). Therefore, studying land-use carbon emissions (LUCE) in the YRB and the decoupling from economic development can help formulate emission reduction strategies. In order to explore the spatiotemporal characteristics of LUCE in the YRB, we estimated the LUCE in 69 cities in the YRB using the downscale energy balance table estimation method and land-use remote sensing data for seven phases from 1990 to 2020. The spatial and temporal features of LUCE were researched from three different spatial scales: the whole spatial scale of the YRB, the sub-basin level, and the city level. Furthermore, the Tapio decoupling model was utilized to research the decoupling state between LUCE and economic development using a multi-scale approach. The Logarithmic Mean Divisia Index (LMDI) model was employed to explore the influencing factors of LUCE in the YRB. These results showed the following: (1) The LUCE in the YRB went through two stages: “stable growth” (1990–2000) and “rapid growth” (2000–2020). The LUCE increased from 165 million tons in 1990 to 1.414 billion tons in 2020, and the average annual growth rate was 25.12%. The spatial pattern of LUCE in the YRB exhibited significant variations, with the LUCE showing a geographic differentiation of midstream > downstream > upstream. (2) Except for the expansive coupling state during 2000–2005 (e: 0.952) and the expansive negative decoupling state during 2015–2020 (e: 2.151), the YRB was in the weak decoupling state for the majority of the time periods. (3) Economic development was the major positive driving factor for the rise of LUCE in this basin, while energy consumption intensity was the primary inhibiting factor. Through a discussion of the features and influencing factors of LUCE, these results can be utilized to provide carbon emission reduction recommendations tailored to the characteristics of cities’ resources and economic development, which will be helpful for achieving low-carbon and sustainable development in the YRB.