Information-based ecological network analysis for carbon emissions

High-resolution carbon emission mapping and spatial-temporal analysis based on multi-source geographic data: A case study in Xi'an City, China

Cities, contributing over 70% of global emissions, are key areas for climate change mitigation. Heterogeneity within cities determines the need for spatialized urban emissions reduction policies. However, few studies have attempted to characterize the spatial distribution of carbon emissions at the urban scale. To address this issue, a novel mapping method was proposed, using Xi'an as an example to explore the spatial distribution of carbon emissions at the city scale. Firstly, multiple geospatial open-source data, such as point of interest (POI), road networks, and land use, were utilized to identify the locations of emission sources. High-resolution carbon emission distributions were then mapped by allocating emissions based on the Intergovernmental Panel on Climate Change (IPCC) methodology. The study employed Global Moran's I to analyze the changes in spatial heterogeneity at different resolutions. Additionally, the Local Indicators of Spatial Association index (LISA) and Standard Deviation Ellipses (SDE) were adopted to examine the spatiotemporal characteristics of carbon emissions in Xi'an. The results show that carbon emissions at Xi'an City rises from 45.112 million tons to 72.701 million tons between 2010 and 2021. The construction of multi-scale carbon emissions spatial distributions, with a resolution of up to 30 m, allowed for a more detailed characterization of carbon emissions, especially in urban fringe areas. In addition, the results indicate that urban carbon emissions exhibit the strongest spatial autocorrelation at a resolution of 350 m. The study can provide a reference for the development of regional carbon emission reduction policies and spatial planning. In addition, the proposed spatialized method of city carbon emissions depends on open-source data, which allows it to have the potential for application in other cities.

Promoting the Achievement of Carbon Neutrality Targets: Systematic Review of Research Dimensions and Measure Methods on Carbon Equity in China

Carbon equity is the balance between carbon reduction responsibilities and development rights. The review of carbon equity in China can help it achieve carbon neutrality targets and provide valuable insights to other emerging countries. This study aimed to systematically sort, classify, compare, and prospect the research dimensions and measure methods for carbon equity. The research dimensions were first classified into intergenerational, regional, trade, and income carbon equity by literature analysis. Intergenerational carbon equity explores the balance of carbon emission rights among generations using integrated assessment models (IAM). Regional carbon equity analyzes the socioeconomic effect of regional carbon emission rights allocation by IAM or regional differences under a specific allocation assumption by the Theil index. Trade carbon equity studies the relationship between carbon emissions and economic benefit transfer embodied in inter-regional trade, which is more suitable for calculating by methods with comparable results, such as the optimized regional environmental inequality index. Income carbon equity investigates the carbon footprint heterogeneity among income groups by the carbon Gini coefficient. This paper further discusses potential research directions for each dimension. Notably, all research dimensions did not consider promoted strategies for carbon equity, which should be a priority for future studies.

Study of spatialtemporal changes in Chinese forest eco-space and optimization strategies for enhancing carbon sequestration capacity through ecological spatial network theory

The conservation of forest ecosystems and the enhancement of carbon sequestration capacity play a crucial role in maintaining ecological balance and human development. However, with excessive deforestation, the flow of energy and information within the ecosystem has changed, which in turn has led to changes in the topological properties and carbon sequestration capacity of forest ecosystems. In order to better investigate the nature and carbon sequestration capacity of forest ecological space in mainland China during 2000-2018, we constructed a time-series Chinese forest ecological spatial network based on complex network theory and graph theory, combined with the modified minimal cumulative resistance model (MCR). By combining the net primary productivity (NPP) values obtained from the Boreal Ecosystem Productivity Simulator (BEPS) model of existing scholars, we further explored the relationship between topology and carbon sequestration capacity within forest ecosystems, and proposed strategies and suggestions for optimization. The results show that forest ecological sources and ecological corridors showed an increasing trend and resistance values decreased year by year during 2000-2018, especially in the western region, indicating that ecological restoration projects in western China have achieved certain effects. However, the stability of forest ecosystems has been decreasing year by year, and the forest carbon sequestration capacity in western China is also decreasing. Through correlation analysis, we found that carbon sequestration capacity showed highly significant positive correlation with closeness centrality, harmonic closeness centrality, clustering, and eigen centrality, and carbon sequestration capacity showed highly significant negative correlation with betweeness centrality. Through Principal Components Analysis (PCA), we suggest that consolidating small patches in the northeast, reducing the number of redundant ecological corridors, adding stepping stone patches to shorten the length of ecological corridors, and increasing ecological corridors in non-northeast areas are conducive to enhancing plant carbon sequestration capacity. This study provides theoretical support and ecological engineering recommendations for China to achieve its strategic goals of carbon neutrality and carbon peaking.

Identification of Key Carbon Emission Industries and Emission Reduction Control Based on Complex Network of Embodied Carbon Emission Transfers: The Case of Hei-Ji-Liao, China

Similar to the problems surrounding carbon transfers that exist in international trade, there are severe carbon emission headaches in regional industrial systems within countries. It is essential for emission reduction control and regional industrial restructuring to clarify the relationship of carbon emissions flows between industrial sectors and identify key carbon-emitting industrial sectors. Supported by the input-output model (I-O model) and social network analysis (SNA), this research adopts input-output tables (2017), energy balance sheets (2021) and the energy statistics yearbooks (2021) of the three Chinese provinces of Hei-Ji-Liao to construct an Embodied carbon emission transfer network (ECETN) and determine key carbon-emitting industrial sectors with a series of complex network measurement indicators and analysis methods. The key abatement control pathways are obtained based on the flow relationships between the chains in the industrial system. The results demonstrate that the ECETNs in all three provinces of Hei-Ji-Liao are small-world in nature with scale-free characteristics (varying according to the power function). The key carbon emission industry sectors in the three provinces are identified through centrality, influence, aggregation and diffusion, comprising coal mining, the chemical industry, metal products industry, machinery manufacturing and transportation in Liaoning Province; coal mining, non-metal mining, non-metal products, metal processing and the electricity industry in Jilin Province; and agriculture, metal processing and machinery manufacturing in Heilongjiang. Additionally, key emission reduction control pathways in the three provinces are also identified based on embodied carbon emission flow relationships between industry sectors. Following the above findings, corresponding policy recommendations are proposed to tackle the responsibility of carbon reduction among industrial sectors in the province. Moreover, these findings provide some theoretical support and policy considerations for policymakers.

Trade and Embodied CO2 Emissions: Analysis from a Global Input-Output Perspective

Global trade drives the world's economic development, while a large amount of embodied carbon is transferred among different countries and regions. Based on a multi-regional input-output model, the trade embodied carbon transfers of bilateral trade between 185 countries/regions around the world were calculated. On the basis, regional trade embodied carbon transfer patterns and major national trade patterns in six continents, eight major economic cooperation organizations, and six representative countries/regions were further analyzed. The results showed that Europe was the continent with the largest embodied carbon inflows from trade and Africa was the continent with the largest embodied carbon outflows from trade. China was the country which had the largest embodied carbon outflows from trade, while the United States, France, Japan, and Germany were countries which had embodied carbon inflows from trade. OECD, EU, and NAFTA were the economic cooperation organizations with embodied carbon inflows from trade, while BRICS, SCO, RCEP, OPEC, and ASEAN were economic cooperation organizations with embodied carbon outflows from trade. Developed countries such as the United States, France, and the United Kingdom protected their environment by exporting high-value products and importing low-value and carbon-intensive products. Developing countries such as China and Russia earned foreign exchange by exporting carbon-intensive and commodity products at a huge environmental cost. In contrast, Germany, China, and Russia played different roles in the global industrial chain, while Germany exchanged more trade surpluses at lower environmental costs. Therefore, for different countries and regions, their own industries should be actively upgraded to adjust the import and export structure, the cooperation and coordination in all regions of the world should be strengthened, and the transfers of embodied carbon needs to be reduced to make the trade model sustainable.

Development of an SMR-induced environmental input-output analysis model - Application to Saskatchewan, Canada

As an emerging power generation technology, small module reactors (SMRs) have the potential for development with its contribution to reducing greenhouse gas (GHG) emissions. In this study, an SMR-induced environmental input-output model (SEIOM) is proposed to simulate the environmental consequences of SMRs development and provide suggested schemes for SMRs deployment. A case study of Saskatchewan, Canada is conducted to demonstrate the proposed model. Specifically, key industries with high reduction potentials are first identified in the study; then, the power supply for three energy-intensive industries is assumed to be replaced by power generated from SMRs at various penetration degrees. The corresponding changes in direct and indirect GHG emissions and the interrelationships among multiple economic sectors associated with GHG flows are analyzed. The results indicate that there are close interdependences between various sectors and a small group of sectors could play a big role in GHG emission mitigation. In Saskatchewan, "Electricity power generation, transmission and distribution", "Oil and gas extraction", "Potash mining" and "Petroleum refineries" are key sectors for realizing GHG emission reduction targets. Meanwhile, it is estimated that replacing the power supply for "Oil and gas extraction" sector with SMRs would contribute the most to the reduction in GHG emission, which is much more than those for "Potash mining" and "Petroleum refineries" sectors. This study is expected to provide a basis for supporting the initiative and application of SMRs.

On the driving factors of China's provincial carbon emission from the view of periods and groups

China is the largest carbon emitter in the world. Understanding carbon emissions of China, especially at the provincial level, will help identify the critical factors behind carbon emissions and effectively implement carbon emission reduction measures. There are significant achievements in the study of carbon emissions of China's provinces. However, there is a gap for improvement in the study from periods and groups' perspectives using a decomposition-clustering method. This paper adopts the Logarithmic Mean Divisia Index (LMDI) to decompose each province's carbon emissions, introduces the elbow and K-means methods to cluster provinces based on the driving factors of decomposition, and analyzes the driving factors of carbon emissions from the view of groups and periods. By analyzing the carbon emissions data of 28 provinces in China from 1998 to 2018, a breakthrough has been found that economic activities and energy intensity were the main driving factors of carbon emissions. Some possible countermeasures, such as optimizing the industrial structure and the energy structure, significantly increasing clean energy consumption, would receive effective carbon emission reduction feedback. The results provide better decision-making support for emission reduction policies in China and contribute to global climate change issues.

Forecasting fuel combustion-related CO2 emissions by a novel continuous fractional nonlinear grey Bernoulli model with grey wolf optimizer

Foresight of CO₂ emissions from fuel combustion is essential for policy-makers to identify ready targets for effective reduction plans and to further improve energy policies and plans. A new method for forecasting the future development of China's CO₂ emissions from fuel combustion is proposed in this paper by using grey forecasting theory. Although the existing fractional nonlinear grey Bernoulli model (denoted as FNGBM(1,1)) has been theoretically proven to enhance the adaptability to diverse sequences, its fixed integer-order differential derivative still impairs the performance to some extent. To this end, a varying-order differential derivative is introduced into the existing differential equation to enable a more flexible structure, thus improving the prediction ability of FNGBM(1,1). Specifically, because of the advantages of conformable fractional accumulation, the traditional differential derivative is first replaced by the conformable fractional differential derivative. As a consequence, the continuous conformable fractional nonlinear grey Bernoulli model (hereinafter referred to as CCFNGBM(1,1)) is proposed. To further increase the validity of the model, a metaheuristic algorithm, namely Grey Wolf Optimizer (GWO), is then applied to search for the optimal emerging coefficients for the proposed model. Two real examples and China's CO₂ emissions from fuel combustion are considered to verify the effectiveness of the newly proposed model, the experimental results show that the newly proposed model outperforms other benchmark models in terms of forecasting accuracy. The proposed model is finally employed to forecast the future China's CO₂ emissions from fuel combustion by 2023, accounting for 10,039.80 million tons. Based on the forecasts, several policy suggestions are provided to curb CO₂ emissions.

What the reclaimed water use can change: From a perspective of inter-provincial virtual water network

Reclaimed water has been used as an alternative water resource for various economic activities, which inevitably is involved in the virtual water trade. However, the effect of reclaimed water on the virtual water trade has not been evaluated in previous studies. For the sake of sustainable water management, this study explored the benefits of reclaimed water use for balancing the water resource allocation at the interprovincial level. Multiregional input-output analysis and ecological network analysis were used to investigate the spatial and structural characteristics of the virtual reclaimed water network (VRWN) among 31 provinces in China and the potential effect of reclaimed water use. The results show that the net export flows of virtual reclaimed water have different spatial patterns from those of freshwater, some provinces that import virtual freshwater are exporters of virtual reclaimed water. Although the exploitative relationship is the dominant ecological relationship in the VRWN (72%), it is confirmed that reclaimed water use contributes to balancing the virtual water trade of China with a more competitive relationship (21%) than in the virtual freshwater network (4%). The virtual freshwater consumption change rate in developed provinces decreases by more than 10% through reclaimed water use. Due to the high food exports and low application of wastewater reclamation in less developed provinces, the effect of reclaimed water use in those provinces is not as obvious as that in developed provinces. This paper offers a new perspective for understanding the current VRWN and guidance for the optimization of the virtual water trade structure.

Global Urban Carbon Networks: Linking Inventory to Modeling

Cities utilize and manipulate an immense amount of global carbon flows through their economic and technical activities. Here, we establish the carbon networks of eight global cities by tracking the carbon exchanges between various natural and economic components. The metabolic properties of these carbon networks are compared by combining flow-based and interpretative network metrics. We further assess the relations of these carbon metabolic properties of cities with their socioeconomic attributes that are deemed important in urban development and planning. We find that, although there is a large difference in city-level carbon balance and flow pattern, a similarity in intercomponent relationships and metabolic characteristicsdoes exist. Cities with lower per capita carbon emissions tend to have healthier metabolic systems with more cooperative resource allocation among various industries, which indicates that there may be synergy between urban decarbonization and carbon-containing resource system optimization. A combination of indicators from flow balance and network models is a promising scheme for linking sector-based carbon inventories to system-based simulations of carbon management efforts. With this done, we may be able to reduce the knowledge gap with respect to how various carbon flows in cities can be concertedly managed considering both the restraint from their climate mitigation goals as well as the impact on urban social and economic development.