High resolution carbon emissions simulation and spatial heterogeneity analysis based on big data in Nanjing City, China

High-resolution mapping of carbon dioxide emissions in Guizhou Province and its scale effects

Accurate spatial distribution of carbon dioxide (CO2) emissions is essential information needed to peaking emissions and achieving carbon neutral in China. The aim of this study was to map CO2 emissions with high spatial resolution at provincial scale and then explore the scale effect on mapping results. As an example, the spatiotemporal pattern and factors influencing CO2 emissions were examined in Guizhou Province in Western China. With the proposed method, a reasonable spatial distribution of CO2 emissions with high spatial resolution was obtained, which had relatively accurate information on spatial details. The optimal resolution of CO2 emissions at the provincial scale under high spatial resolution was approximately 90 m and 1260 m. More detailed grid data can better reflect the spatial variability of CO2 emissions. Emissions of CO2 were spatially heterogeneous in Guizhou, with high emissions in centers of big cities that gradually spread and decreased from city centers. From 2009 to 2019, the spatial distribution of CO2 emissions developed from agglomeration to dispersion. Areas of high carbon emissions decreased, those of medium carbon emissions increased, and many areas changed from no emissions to carbon emissions. Industrial land had the highest emissions, followed by commercial and transportation lands. Over 10 years, changes occurred in the relation between interregional economic level of Guizhou and CO2 emissions, with the relation changing from linear into an inverted U-shaped relation. The effect of industrial structure on CO2 emissions decreased, and the linear increase between CO2 emissions and the urban scale became more evident. The results of this study will contribute to accurate monitoring and management of carbon emissions in Guizhou, as well as provide support to formulate policies related to controls on carbon emissions in different regions.

The impact of teleconnections of built-up land on regional carbon burden under the shared socio-economic pathways

The expansion of built-up land is currently being increasingly triggered by remote demand, thus disturbing the local process of carbon neutrality significantly. It is meaningful to understand the relations between regional development and carbon balance. To this end, we combine the multi-regional input-output model with the land system cellular automata model for potential effects (LANDSCAPE) to illustrate the impact that regional development has on the carbon burden. The results show that the expansion of built-up land results in a regional carbon burden through taking over ecological land and generating carbon emissions, to which the manufacturing industry land is the largest contributor. Regionally, developed regions exert the greatest influences on the changes in the regional carbon burden, mainly because the promotion of their development leads to the expansion of built-up land in all regions. Developing regions can impact undeveloped regions and themselves, while it is hard for undeveloped regions to change the regional carbon burden due to their low capacity to externally drive the expansion of built-up land. Meanwhile, the continuing development of developed regions exerts great pressure on carbon neutrality in both developing and undeveloped regions as they expand the "high-quality" built-up land themselves, which means that regional development may lead to changes in the carbon burden of regions which are less developed.

Explore the spatial pattern of carbon emissions in urban functional zones: a case study of Pudong, Shanghai, China

It is crucial for the development of carbon reduction strategies to accurately examine the spatial distribution of carbon emissions. Limited by data availability and lack of industry segmentation, previous studies attempting to model spatial carbon emissions still suffer from significant uncertainty. Taking Pudong New Area as an example, with the help of multi-source data, this paper proposed a research framework for the amount calculation and spatial distribution simulation of its CO2 emissions at the scale of urban functional zones (UFZs). The methods used in this study were based on mapping relations among the locations of geographic entities and data of multiple sources, using the coefficient method recommended by the Intergovernmental Panel on Climate Change (IPCC) to calculate emissions. The results showed that the emission intensity of industrial zones and transport zones was much higher than that of other UFZs. In addition, Moran's I test indicated that there was a positive spatial autocorrelation in high emission zones, especially located in industrial zones. The spatial analysis of CO2 emissions at the UFZ scale deepened the consideration of spatial heterogeneity, which could contribute to the management of low carbon city and the optimal implementation of energy allocation.

Rasterizing CO2 emissions and characterizing their trends via an enhanced population-light index at multiple scales in China during 2013-2019

Climate change caused by CO2 emissions (CE) has received widespread global concerns. Obtaining precision CE data is necessary for achieving carbon peak and carbon neutrality. Significant deficiencies of existing CE datasets such as coarse spatial resolution and low precision can hardly meet the actual requirements. An enhanced population-light index (RPNTL) was developed in this study, which integrates the Nighttime Light Digital Number (DN) Value from the National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) and population density to improve CE estimation accuracy. The CE from the Carbon Emission Accounts & Datasets (CEADS) was divided into three sectors, namely urban, industrial, and rural, to differentiate the heterogeneity of CE in each sector. The ordinary least square (OLS), geographically weighted regression (GWR), temporally weighted regression (TWR), and geographically and temporally weighted regression (GTWR) models were employed to establish the quantitative relationship between RPNTL and CE for each sector. The optimal model was determined through model comparison and precision evaluation and was utilized to rasterize CE for urban, industrial, and rural areas. Additionally, hot spot analysis, trend analysis, and standard deviation ellipses were introduced to demonstrate the spatiotemporal dynamic characteristics of CE at multiple scales. The performance of the GTWR outperformed other methods in estimating CE. The enhanced RPNTL demonstrated a higher coefficient of determination (R2 = 0.95) than the NTL (R2 = 0.92) in predicting CE, particularly in rural regions where the R2 value increased from 0.76 to 0.81. From 2013 to 2019, high CE was observed in eastern and northern China, while a decreasing trend was detected in northeastern China and Chengdu-Chongqing. Conversely, the Yangtze River Delta, Pearl River Delta, Fenwei Plain, and Henan Province showed an increasing trend. The center of gravity for industrial and rural CE is shifting towards western regions, whereas that for urban CE is moving northward. This study provides valuable insights for decision-making on CE control.

Study on the spatial distribution of urban carbon emissions at the micro level based on multisource data

Global warming is currently an area of concern. Human activities are the leading cause of urban greenhouse gas intensification. Inversing the spatial distribution of carbon emissions at microscopic scales such as communities or controlling detailed planning plots can capture the critical emission areas of carbon emissions, thus providing scientific guidance for intracity low-carbon development planning. Using the Sino-Singapore Tianjin Eco-city as an example, this paper uses night-light images and statistical yearbooks to perform linear fitting within the Beijing-Tianjin-Hebei city-county region and then uses fine-scale data such as points of interest, road networks, and mobile signaling data to construct spatial characteristic indicators of carbon emissions distribution and assign weights to each indicator through the analytic hierarchy process. As a result, the spatial distribution of carbon emissions based on detailed control planning plots is calculated. The results show that among the selected indicators, the population distribution significantly influences carbon emissions, with a weight of 0.384. The spatial distribution of carbon emissions is relatively distinctive. The primary carbon emissions are from the Sino-Singapore Cooperation Zone due to its rapid urban construction and development. In contrast, carbon emissions from other areas are sparse, as there is mostly unused land under construction.

Towards sustainable development: what carbon trading pilot policy has been done for mitigating carbon emissions and air pollution?

This study examines the impact of carbon trading pilot policy (CTPP) on carbon emissions (CO2) and air pollution (Ap) using the difference in differences method (DID) utilizing panel data from 30 Chinese areas spanning from 2008 to 2020. The results indicate that CTPP implementation can effectively decrease CO2 and Ap. CTPP can reduce CO2 and Ap through positive incentive effects that promote industrial structure upgrading and drive technological progress. Moreover, CTPP exhibits significant regional variation, with CTPP significantly reducing CO2 in both the eastern and central and western regions. CTPP do not show an effective reduction in Ap in eastern region, while effectively reduce Ap in central and western regions.

How do resource dependence and technological progress affect carbon emissions reduction effect of industrial structure transformation? Empirical research based on the rebound effect in China

Under the guidance of carbon peak and carbon neutral targets, the industrial structure transformation is vital for carbon emissions reduction in China. However, there is a rebound effect of carbon emissions during the industrial structure transformation. Resource dependence and technological progress have significant impacts on industrial structure transformation and its carbon reduction effect. This paper explores how industrial structure transformation under resource dependence causes the rebound effect from a technological progress perspective. The key results indicate that (1) resource dependence distorts the carbon emissions reduction effect of industrial structure transformation; (2) with the development of technology, the industrial structure upgrading under resource dependence could cause an increase on carbon emissions at the beginning, but the increase would be weakened subsequently, displaying a two-stage feature; (3) the industrial structure rationalization under resource dependence reduces carbon emissions at first, but the reduction would be weakened as the technology develops, then industrial structure's rationalization shows an insignificant impact on carbon emissions, and finally reduces carbon emissions again, presenting a four-stage characteristic; (4) environmental protection technology can correct the distortion effect of resource dependence on the industrial structure rationalization and amplify the industrial structure rationalization's reduction effects on carbon emissions; (5) with the development of energy-saving technology, industrial structure rationalization has a paradoxical impact on carbon emissions, the industrial structure rationalization first reduces, then increases, and finally reduces carbon emissions, indicating an inverted "N" relationship. Finally, policy recommendations for carbon emissions reduction are proposed from the perspective of industrial structure transformation and technological progress.

Carbon mitigation potential afforded by rooftop photovoltaic in China

Rooftop photovoltaics (RPVs) are crucial in achieving energy transition and climate goals, especially in cities with high building density and substantial energy consumption. Estimating RPV carbon mitigation potential at the city level of an entire large country is challenging given difficulties in assessing rooftop area. Here, using multi-source heterogeneous geospatial data and machine learning regression, we identify a total of 65,962 km² rooftop area in 2020 for 354 Chinese cities, which represents 4 billion tons of carbon mitigation under ideal assumptions. Considering urban land expansion and power mix transformation, the potential remains at 3-4 billion tons in 2030, when China plans to reach its carbon peak. However, most cities have exploited less than 1% of their potential. We provide analysis of geographical endowment to better support future practice. Our study provides critical insights for targeted RPV development in China and can serve as a foundation for similar work in other countries.

The effects of urban land use on energy-related CO2 emissions in China

Land use change caused by urbanization is widely believed to be the primary way human activities affect energy use and, thus, CO₂ emissions (CEs) in China. However, there is a limited understanding of the role of land use with detailed categories in energy-related CEs is still absent. This paper aims to narrow the knowledge gap using multi-dimension metrics, including land use scale, mixture, and intensity. These metrics were derived from three years of sequential POI data. A GWR analysis was carried out to examine the associations between land use change and energy-related CEs. Our results show that (1) the scale of most land use types exerted a bidirectional effect on CEs, demonstrating apparent spatiotemporal heterogeneity; (2) land use mixture of mature city agglomerations had a significant suppressive effect on CEs, suggesting mixed land use be advocated in the urbanization process; (3) Land use intensity had a bi-directional association with CEs in most cities, but its adverse effect gradually spread from the west to the northeast. Therefore, systematically regulating land transaction to control land scale, appropriately interplanting biofuel plants, and utilizing renewable energy are encouraged to reduce energy footprints and mitigate CEs in China. The findings and conclusions of this paper enhance our knowledge on the relationship between land use and CEs and present the scientific basis for policy-making in building low-carbon cities in the context of rapidly urbanizing China.

How does technological innovation affect carbon emission efficiency in the Yellow River Economic Belt: the moderating role of government support and marketization

The Yellow River Economic Belt (YREB) is a fundamental ecological protection barrier for China. Its carbon pollution issues are currently severe owing to the extensive energy consumption and unsatisfactory industrial constructions. In this context, this paper estimates carbon emission efficiency (CEE) based on the panel data from 56 cities in the YREB during the period 2006-2019 and analyzes its spatial distribution characteristics. Additionally, the spatial Durbin model (SDM) is utilized to examine the effect of technological innovation (TI) on CEE as a result of the moderating effects of government support (GS) and marketization (MA), respectively. The results indicated that (i) in the YREB, CEE exhibited significant spatial autocorrelation characteristics; (ii) TI negatively affected local CEE; (iii) the moderating effect of local GS on the relationship between TI and CEE in the local area was negative, but its spatial spillover effect was still not significant; (iv) the moderating effect of local MA on the relationship between TI and CEE in the local area was also negative, but positive in the surrounding areas. Based on the empirical analysis, a series of policy suggestions are proposed to improve the YREB's CEE.

Spatially explicit carbon emissions by remote sensing and social sensing

Scientific simulation of carbon emissions is an important prerequisite for achieving low-carbon green development and carbon peak and carbon neutralization. This study proposed a carbon emissions spatialization method based on nighttime light (NTL) remote sensing and municipal electricity social sensing. First, the economics-energy comprehensive index (EECI) was proposed by integrating the NTL and municipal electricity consumption (EC) data. Second, the carbon emissions were spatialized at a fine scale based on NTL, EC, and EECI, respectively. Finally, the geographical detector model was applied to quantify the influencing factors on carbon emissions from the perspectives of individuals and interactions. Results show that combining remote sensing and social sensing data helps depict carbon emissions accurately. The factor analysis found that GDP and population were the basis of carbon emissions, while the secondary industry and urbanization rate were the direct factors. This study is expected to provide constructive suggestions and methods for emission reduction, carbon peak, and carbon neutrality in high-density cities in China.

Effects of urban land intensive use on carbon emissions in China: spatial interaction and multi-mediating effect perspective

Land intensive use is important for sustainable land use while carbon emission is a constraint to achieve carbon neutrality; they are closely related and committed to sustainable socioeconomic development. The spatial interactions and complex mechanisms between them make it difficult to clarify their relationship precisely. This paper combines the spatial Durbin model and mediating effect model to describe the effects of urban land intensive use (ULIU) on carbon emissions (CEs) for 30 provinces in China during 1995-2018. An inverse U-shaped relationship between ULIU and CEs is proved while considering the spatial interaction. All the provincial observed values of ULIU are less than the inflection point of the curve, which means that the improvement of the ULIU will cause more CEs. Meanwhile, ULIU has multi-indirect effects on CEs, with urbanization and industrial structure upgrading playing mediating roles in the mechanism. The spatial spillover effects of CEs per unit area on the neighboring provinces are negative, indicating that the interregional influence cannot be ignored. These findings have theoretical and practical significance as they reveal the multi-effects of ULIU on CEs. To realize carbon emission reduction effectively, more attention should be paid to improving the intensity of the urban land and strengthening the regional cooperation.

Assessment of Urban Flood Vulnerability Using the Integrated Framework and Process Analysis: A Case from Nanjing, China

Flooding is a serious challenge that increasingly affects residents as well as policymakers. Many studies have noted that decreasing the urban flood vulnerability (UFV) is an indispensable strategy for reducing flood risks; however, some studies have several pertinent assessment limitations. The objective of this study is to assess the UFV of the Xuanwu-Qinhuai-Jianye-Gulou-Yuhua (XQJGY) region from 2012 to 2018 by integrating various indicators into a composite index. This study uses the environment for visualizing images (ENVI) and the geographic information system (GIS) to extract indicators that have geographic attributes for the assessment of UFV and the process analysis method is then used to explore the relationship between these indicators. The results indicated that: (1) The UFV of Xuanwu, Qinhuai, and Gulou decreased from 2012 to 2018 and the UFV of Jianye and Gulou increased from 2012 to 2015 and decreased from 2015 to 2018. (2) The vegetation coverage, precipitation during the flood season, population density, and highway density significantly contributed to the UFV. (3) There also exist transformation pathways between the indicators that led to vulnerability in five districts. This study provides a theoretical basis for the government to manage floods.

Vector maps and spatial autocorrelation of carbon emissions at land patch level based on multi-source data

An accurate carbon emissions map is of great significance for urban planning to reduce carbon emissions, mitigate the heat island effect, and avoid the impact of high temperatures on human health. However, little research has focused on carbon emissions maps at the land patch level, which makes poor integration with small and medium-sized urban planning based on land patches. In this study, a vectorization method for spatial allocation of carbon emissions at the land patch level was proposed. The vector maps and spatial autocorrelation of carbon emissions in Zhangdian City, China were explored using multi-source data. In addition, the differences between different streets were analyzed, and the carbon emissions ratio of the land patch was compared. The results show that the vector carbon emissions map can help identify the key carbon reduction land patches and the impact factors of carbon emissions. The vector maps of Zhangdian City show that in 2021, the total carbon emissions and carbon absorptions were 4.76 × 10⁹kg and 4.28 × 10⁶kg respectively. Among them, industrial land accounted for 70.16% of carbon emissions, mainly concentrated in three industrial towns. Forest land carbon absorption accounted for 98.56%, mainly concentrated in the peripheral streets away from urban areas. The Moran's I of land patch level carbon emissions was 0.138, showing a significant positive spatial correlation. The proportion of land patches is an important factor in determining carbon emissions, and the adjustment of industrial structure is the most critical factor in reducing carbon emissions. The results achieved can better help governments develop different carbon reduction strategies, mitigate the heat island effect, and support low-carbon and health-oriented urban planning.

The Impact of Urbanization Growth Patterns on Carbon Dioxide Emissions: Evidence from Guizhou, West of China

Little attention has been paid to the impact of future urban expansion patterns on carbon emissions based on the existing urban pattern of a region. This study used the Central Guizhou Urban Agglomeration as the study area, and the relationships between regional urbanization and CO2 emissions in the study area were analyzed based on historical data. Urban growth patterns were then simulated in four scenarios that focused on the next 15 years, and they were based on the cellular automaton model. In each different scenario, the CO2 emissions were predicted, and some implications regarding the impact of those emissions were provided. The results showed that as urban land-use intensity increases, CO2 emissions first increase then decrease; however, the rate of decline for CO2 emissions is much slower than the rate at which it rises. Moreover, in the next 15 years, urban expansion will lead to a significant increase in CO2 emissions. The CO2 emissions were found to be lowest in the spatial agglomeration scenario and highest in the spatial dispersion scenario. The spatial agglomeration scenario was conducive to understanding how CO2 emissions eventually peak; however, different cities in the study area should adopt different urban expansion patterns. These research results can provide a reference guide for the government with regard to urban planning.

Research and Analysis on the Influencing Factors of China’s Carbon Emissions Based on a Panel Quantile Model

Since the beginning of the new century, China’s carbon emissions have increased significantly, and the country has become the world’s largest carbon emitter. Therefore, determining the influencing factors of carbon emissions is an important issue for policymakers. Based on the panel data of 30 provinces and cities across the country from 2000 to 2018, this study empirically tested how per capita disposable income, industrial structure, urbanization level, average family size, and technological innovation level impacts carbon emissions at different quantile levels by using the panel quantile STIRPAT model. The results showed that per capita disposable income and industrial structure had significant promoting effects on carbon emissions, while urbanization level, average family size, and technological innovation level had significant inhibitory effects on carbon emissions. The main thing is that the emission distributions of the 10th and 90th quantiles of the independent variables were quite different, which shows that the influence of each factor on carbon emissions has obvious heterogeneity at different levels. Specifically, the impact of per capita disposable income and technological innovation level on carbon emissions in low carbon emission areas were higher than that in high carbon emission areas, and the impact of industrial structure, urbanization level, and average household size on carbon emissions in high carbon emission areas was higher. Finally, specific policy implications are provided based on these results.

A fine spatial resolution modeling of urban carbon emissions: a case study of Shanghai, China

Quantification of fossil fuel carbon dioxide emissions (CEs) at fine space and time resolution is a critical need in climate change research and carbon cycle. Quantifying changes in spatiotemporal patterns of urban CEs is important to understand carbon cycle and development carbon reduction strategies. The existing spatial data of CEs have low resolution and cannot distinguish the distribution characteristics of CEs of different emission sectors. This study quantified CEs from 15 types of energy sources, including residential, tertiary, and industrial sectors in Shanghai. Additionally, we mapped the CEs for the three sectors using point of interest data and web crawler technology, which is different from traditional methods. At a resolution of 30 m, the improved CEs data has a higher spatial resolution than existing studies. The spatial distribution of CEs based on this study has higher spatial resolution and more details than that based on traditional methods, and can distinguish the spatial distribution characteristics of different sectors. The results indicated that there was a consistent increase in CEs during 2000–2015, with a low rate of increase during 2009–2015. The intensity of CEs increased significantly in the outskirts of the city, mainly due to industrial transfer. Moreover, intensity of CEs reduced in city center. Technological progress has promoted the improvement of energy efficiency, and there has been a decoupling between the economic development and CEs in the city was observed during in 2000–2015.

Carbon emissions from land use in Jiangsu, China, and analysis of the regional interactions

Land carbon emissions are primarily determined by land use type, and these emissions could be transferred during interprovincial trade activities. This study took Jiangsu in China as a case, assigned all the energy-related carbon emissions to land, and analyzed the transferred land use carbon emissions through the application of a tele-coupling framework. Finally, the physical spatial distribution of transferred land use carbon emissions within Jiangsu at high resolution was simulated. China and Jiangsu emitted 2.27 × 10⁹ t and 1.43 × 10⁸ t of carbon in 2012, respectively, with industrial and mining land being the biggest emission source, generating more than 70% of their total emissions. Overall, Jiangsu's net carbon emissions transferred to other provinces was 2.41 × 10⁶ t in urban land and 9.03 × 10⁵ t in industrial and mining land, and these carbon emissions were mainly transferred to Hebei, Shandong, and Inner Mongolia. Land utilization intensity and economic development influenced the carbon emission transfer to some extent. Other provinces also transferred a large amount of carbon emissions to Jiangsu, of which 2.57 × 10⁶ t was in urban land and 3.18 × 10⁷ t was in industrial and mining land. Our simulation showed that the emissions in both land use types exhibited a south-north difference within Jiangsu; more specifically, urban land carbon emissions were mainly concentrated in core urban areas, especially in Suzhou, Wuxi, and Nanjing, whereas industrial and mining land carbon emissions were mostly distributed in the periphery of core urban areas and along the Yangtze River. To balance economic development and environment protection, the government must limit the expansion of construction land (especially industrial and mining land), and developed regions should implement various types of ecological compensation measures to help less developed regions reduce carbon embodied in trade activities.

Detecting Differences in the Impact of Construction Land Types on Carbon Emissions: A Case Study of Southwest China

The area with the highest concentration of carbon emission activities is construction land. However, few studies have been conducted that investigated the different effects of various types of construction land on carbon emissions and the extent of their impact. To address this shortcoming, this study constructed a multi-indicator evaluation system with 393 counties in Southwest China and integrated ordinary least squares and spatial regression models to deeply analyze the different impacts of construction land types on carbon emissions. The results revealed that (1) in Southwest China, carbon emissions were generally distributed in clusters, with significant spatial variability and dependence; (2) the distribution of urban land scale, rural settlement land scale, and other construction land scale all showed obvious spatial clustering differences; (3) all three types of construction land’s effect on carbon emissions was positive, and the direction of impact was in line with theoretical expectations; and (4) the other construction land scale had the highest effect on carbon emissions, followed by rural settlement land scale, while the urban land scale was slightly lower. The findings help to further explain the different impacts of construction land types on carbon emissions and provide theoretical references for the government to formulate more refined emissions reduction policies.

Built Environment Factors (BEF) and Residential Land Carbon Emissions (RLCE)

Evaluating the effects of built environment factors (BEF) on residential land carbon emissions (RLCE) is an effective way to reduce RLCE and promote low-carbon development from the perspective of urban planning. In this study, the Grey correlation analysis method and Universal global optimization method were proposed to explore the effects of BEF on RLCE using advanced metering infrastructure (AMI) data in Zibo, a representative resource-based city in China. The results indicated that RLCE can be significantly affected by BEF such as intensity, density, morphology, and land. The morphology is the most critical BEF in reducing RLCE. Among them, the building height (BH) and building shape coefficient (BSC) had positive effects on RLCE, while the high-rise buildings ratio (HRBR) and RLCE decreased first and then increased. The R2 of BH, BSC, and HRBR are 0.684, 0.754, and 0.699. The land had limited effects in reducing RLCE, and the R2 of the land construction time (LCT) is only 0.075, which has the least effect on RLCE. The results suggest that urban design based on BEF optimization would be effective in reducing the RLCE.

Study on the Emission Reduction Effect and Spatial Difference of Carbon Emission Trading Policy in China

To cope with huge carbon emission pressure, China has implemented a carbon emissions trading pilot policy that aims to provide reasonable suggestions for the smooth operation of the national carbon market. This paper selects the provincial panel data in China from 2005 to 2019 and uses the propensity score matching-difference in difference (PSM-DID) method to evaluate the carbon emission policy’s reduction effect. Based on carbon emissions (CE) and carbon emission intensity (CI), provinces and cities are divided into four regions, and each region is verified by spatial difference analysis. Furthermore, the mediating effects of carbon emission reduction through the dual aspects of technological progress and industry structure are also discussed. Results verified that, (1) under the carbon emission trading policy, regional carbon emissions and carbon emission intensity are both significantly reduced. (2) Technological progress helps to reduce carbon emissions, while industrial structure shows no obvious contribution. (3) The four regions all show ideal emission reduction effects, of which the High CE-High CI region shows the best, but is greatly restricted by techniques. The industrial structure of the High CE-Low CI region needs to be further optimized for carbon reduction. In the Low CE-High CI region, the carbon emissions brought by economic development fail to effectively improve per capita GDP. The Low CE-Low CI region contributes greatly to carbon emission reduction with technical advantages.

Preventing a rebound in carbon intensity post-COVID-19 - lessons learned from the change in carbon intensity before and after the 2008 financial crisis

The carbon emission rebound of the post-2008 financial crisis teaches us a lesson that avoiding a rebound in carbon intensity is key to prevent the carbon emission increase afterward. Although how carbon emission will change the world after the COVID-19 pandemic is unknown, it is urgent to learn from the past and avert or slow down the potential rebound effect. Therefore, this study aims to identify key drivers of carbon intensity changes of 55 sectors, applying the decomposition techniques and the world input-output data. Our results demonstrate that global carbon intensity fluctuates drastically when shocked by the global financial crisis, presenting an inversed-V shape for the period 2008-2011. Industrial carbon emission and gross output vary among different industries, the growth rate of industrial carbon intensity varies from -55.55% to 23.77%. The energy intensity effect and economic structure effect have opposite impacts on carbon intensity decrease, accelerating and hindering the decreasing carbon intensity, respectively. However, the energy mix effect has a minor impact on carbon intensity decrease. The industrial carbon intensity decomposition results show the impact of technological and structural factors are significantly different among industries. Moreover, the impact of energy intensity is slightly stronger than the energy mix. More measures targeting avoiding the rebound in carbon intensity should be developed.

Leveraging big data analytics capabilities in making reverse logistics decisions and improving remanufacturing performance

Purpose

The study investigated the effect of big data analytics capabilities (BDACs) on reverse logistics (strategic and tactical) decisions and finally on remanufacturing performance.

Design/methodology/approach

The primary data were collected using a structured questionnaire and an online survey sent to South African manufacturing companies. The data were analysed using partial least squares based structural equation modelling (PLS–SEM) based WarpPLS 6.0 software.

Findings

The results indicate that data generation capabilities (DGCs) have a strong association with strategic reverse logistics decisions (SRLDs). Data integration and management capabilities (DIMCs) show a positive relationship with tactical reverse logistics decisions (TRLDs). Advanced analytics capabilities (AACs), data visualisation capabilities (DVCs) and data-driven culture (DDC) show a positive association with both SRLDs and TRLDs. SRLDs and TRLDs were found to have a positive link with remanufacturing performance.

Practical implications

The theoretical guided results can help managers to understand the value of big data analytics (BDA) in making better quality judgement of reverse logistics and enhance remanufacturing processes for achieving sustainability.

Originality/value

This research explored the relationship between BDA, reverse logistics decisions and remanufacturing performance. The study was practice oriented, and according to the authors’ knowledge, it is the first study to be conducted in the South African context.

Digital Transformation and Environmental Sustainability: A Review and Research Agenda

Digital transformation refers to the unprecedented disruptions in society, industry, and organizations stimulated by advances in digital technologies such as artificial intelligence, big data analytics, cloud computing, and the Internet of Things (IoT). Presently, there is a lack of studies to map digital transformation in the environmental sustainability domain. This paper identifies the disruptions driven by digital transformation in the environmental sustainability domain through a systematic literature review. The results present a framework that outlines the transformations in four key areas: pollution control, waste management, sustainable production, and urban sustainability. The transformations in each key area are divided into further sub-categories. This study proposes an agenda for future research in terms of organizational capabilities, performance, and digital transformation strategy regarding environmental sustainability.

A Vector Map of Carbon Emission Based on Point-Line-Area Carbon Emission Classified Allocation Method

An explicit spatial carbon emission map is of great significance for reducing carbon emissions through urban planning. Previous studies have proved that, at the city scale, the vector carbon emission maps can provide more accurate spatial carbon emission estimates than gridded maps. To draw a vector carbon emission map, the spatial allocation of greenhouse gas (GHG) inventory is crucial. However, the previous methods did not consider different carbon sources and their influencing factors. This study proposes a point-line-area (P-L-A) classified allocation method for drawing a vector carbon emission map. The method has been applied in Changxing, a representative small city in China. The results show that the carbon emission map can help identify the key carbon reduction regions. The emission map of Changxing shows that high-intensity areas are concentrated in four industrial towns (accounting for about 80%) and the central city. The results also reflect the different carbon emission intensity of detailed land-use types. By comparison with other research methods, the accuracy of this method was proved. The method establishes the relationship between the GHG inventory and the basic spatial objects to conduct a vector carbon emission map, which can better serve the government to formulate carbon reduction strategies and provide support for low-carbon planning.

Investigating PM2.5 responses to other air pollutants and meteorological factors across multiple temporal scales

It remains unclear on how PM_2.5 interacts with other air pollutants and meteorological factors at different temporal scales, while such knowledge is crucial to address the air pollution issue more effectively. In this study, we explored such interaction at various temporal scales, taking the city of Nanjing, China as a case study. The ensemble empirical mode decomposition (EEMD) method was applied to decompose time series data of PM_2.5, five other air pollutants, and six meteorological factors, as well as their correlations were examined at the daily and monthly scales. The study results show that the original PM_2.5 concentration significantly exhibited non-linear downward trend, while the decomposed time series of PM_2.5 concentration by EEMD followed daily and monthly cycles. The temporal pattern of PM₁₀, SO₂ and NO₂ is synchronous with that of PM_2.5. At both daily and monthly scales, PM_2.5 was positively correlated with CO and negatively correlated with 24-h cumulative precipitation. At the daily scale, PM_2.5 was positively correlated with O₃, daily maximum and minimum temperature, and negatively correlated with atmospheric pressure, while the correlation pattern was opposite at the monthly scale.

Do energy technology innovations contribute to CO2 emissions abatement? A spatial perspective

With growing concerns about global warming, energy technology innovation has attracted attention from scholars. Many studies have explored the relationship between energy technology innovations and energy consumption, while the influence of energy technology innovation on carbon emissions (CEs) has not received enough attention. Utilizing the spatial econometric model, this study aims to examine whether energy technology innovations are beneficial for CO₂ emissions abatement in China. The results indicate that, first, renewable energy technology innovation facilitates CO₂ abatement, while fossil energy technology innovation is ineffective in reducing CEs. Second, the influences of energy technology innovations on CEs are trans-regional. Third, economic growth would agglomerate CEs from low-growth province to neighboring high-growth provinces; mandatory environmental regulation in China would transfer CEs from provinces with strict regulations to neighboring provinces with loose regulation. Based on these findings, a series of policy recommendations are put forward to tackle China's CEs. One possible innovation is that this study considers geographic location when investigating how energy technology innovations affect CEs.

Distribution network model using big data in an international environment

This paper proposes dynamic mixed integer facility location model to design an international manufacturing network (IMN). The proposed model considers a broad facility network linking production and distribution facilities located internationally. The proposed model discussed in the paper assumes significance over the traditional manufacturing model as it provides a country specific analysis making it more convenient for the decision maker to devise country specific strategies within an international ecosystem. Therefore, the model considers import export cost, loan subsidies along with depreciation expense and other operating costs applicable to specific country. The objective of the model is to identify optimal facility locations and the production distribution in the entire network to meet the demand of global markets. The proposed model is illustrated and computationally tested using two cases. Model parameters are mapped using 3Vs of Big Data viz. Volume, Velocity and Variety.