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This study seeks to estimate the carbon implications of recent changes in China’s economic development patterns and role in global trade in the post-financial-crisis era. We utilised the latest socioeconomic datasets to compile China’s 2012 multiregional input-output (MRIO) table. Environmentally extended input-output analysis and structural decomposition analysis (SDA) were applied to investigate the driving forces behind changes in CO 2 emissions embodied in China’s domestic and foreign trade from 2007 to 2012. Here we show that emission flow patterns have changed greatly in both domestic and foreign trade since the financial crisis. Some economically less developed regions, such as Southwest China, have shifted from being a net emission exporter to being a net emission importer. In terms of foreign trade, emissions embodied in China’s exports declined from 2007 to 2012 mainly due to changes in production structure and efficiency gains, while developing countries became the major destination of China’s export emissions. China has entered a new normal phase of economic development with a changing role in global trade. Here the authors show that emissions embodied in China’s exports declined from 2007 to 2012, while developing countries become the major destinations of China’s export emissions.

China has entered the economic transition in the post-financial crisis era, with unprecedented new features that significantly lead to a decline in its carbon emissions. However, regional disparity implies different trajectories in regional decarbonisation. Here, we construct multi-regional input-output tables (MRIO) for 2012 and 2015 and quantitatively evaluate the regional disparity in decarbonisation and the driving forces during 2012-2015. We found China's consumption-based emissions peaked in 2013, largely driven by a peak in consumption-based emissions from developing regions. Declined intensity and industrial structures are determinants due to the economic transition. The rise of the Southwest and Central regions of China have become a new feature, driving up emissions embodied in trade and have reinforced the pattern of carbon flows in the post-financial crisis period. Export-related emissions have bounced up after years of decline, attributed to soaring export volume and export structure in the Southeast and North of the country. The disparity in developing regions has become the new feature in shaping China's economy and decarbonisation.

With the implementation of China’s top-down CO2 emissions reduction strategy, the regional differences should be considered. As the most basic governmental unit in China, counties could better capture the regional heterogeneity than provinces and prefecture-level city, and county-level CO2 emissions could be used for the development of strategic policies tailored to local conditions. However, most of the previous accounts of CO2 emissions in China have only focused on the national, provincial, or city levels, owing to limited methods and smaller-scale data. In this study, a particle swarm optimization-back propagation (PSO-BP) algorithm was employed to unify the scale of DMSP/OLS and NPP/VIIRS satellite imagery and estimate the CO2 emissions in 2,735 Chinese counties during 1997–2017. Moreover, as vegetation has a significant ability to sequester and reduce CO2 emissions, we calculated the county-level carbon sequestration value of terrestrial vegetation. The results presented here can contribute to existing data gaps and enable the development of strategies to reduce CO2 emissions in China.Measurement(s)carbon dioxide emission • carbon dioxide sequestrationTechnology Type(s)machine learningFactor Type(s)temporal interval • geographic locationSample Characteristic - Environmentcarbon dioxideSample Characteristic - LocationChinaMachine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13090370

As fundamental data, gross domestic product (GDP) and electricity consumption can be used to effectively evaluate economic status and living standards of residents. Some scholars have estimated gridded GDP and electricity consumption. However, such gridded data have shortcomings, including overestimating real GDP growth, ignoring the heterogeneity of the spatiotemporal dynamics of the grid, and limited time-span. Simultaneously, the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) and National Polar-orbiting Partnership’s Visible Infrared Imaging Radiometer (NPP/VIIRS) nighttime light data, adopted in these studies as a proxy tool, still facing shortcomings, such as imperfect matching results, discontinuity in temporal and spatial changes. In this study, we employed a series of methods, such as a particle swarm optimization-back propagation (PSO-BP) algorithm, to unify the scales of DMSP/OLS and NPP/VIIRS images and obtain continuous 1 km × 1 km gridded nighttime light data during 1992–2019. Subsequently, from a revised real growth perspective, we employed a top-down method to calculate global 1 km × 1 km gridded revised real GDP and electricity consumption during 1992–2019 based on our calibrated nighttime light data.Measurement(s)GDP • electricty consumptionTechnology Type(s)machine learning

Accurate, long-term, full-coverage carbon dioxide (CO 2 ) data in units of prefecture-level cities are necessary for evaluations of CO 2 emission reductions in China, which has become one of the world’s largest carbon-emitting countries. This study develops a novel method to match satellite-based Defense Meteorological Satellite Program’s Operational Landscan System (DMSP/OLS) and Suomi National Polar-orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) nighttime light data, and estimates the CO 2 emissions of 334 prefecture-level cities in China from 1992 to 2017. Results indicated that the eastern and coastal regions had higher carbon emissions, but their carbon intensity decreased more rapidly than other regions. Compared to previous studies, we provide the most extensive and long-term CO 2 dataset to date, and these data will be of great value for further socioeconomic research. Specifically, this dataset provides a foundational data source for China’s future CO 2 research and emission reduction strategies. Additionally, the methodology can be applied to other regions around the world.

Global production fragmentation generates indirect socioeconomic and environmental impacts throughout its expanded supply chains. The multi-regional input-output model (MRIO) is a tool commonly used to trace the supply chain and understand spillover effects across regions, but often cannot be applied due to data unavailability, especially at the sub-national level. Here, we present MRIO tables for 2012, 2015, and 2017 for 31 provinces of mainland China in 42 economic sectors. We employ hybrid methods to construct the MRIO tables according to the available data for each year. The dataset is the consistent China MRIO table collection to reveal the evolution of regional supply chains in China’s recent economic transition. The dataset illustrates the consistent evolution of China’s regional supply chain and its economic structure before the 2018 US-Sino trade war. The dataset can be further applied as a benchmark in a wide range of in-depth studies of production and consumption structures across industries and regions. Measurement(s) multi-regional input-output Technology Type(s) partial survey Factor Type(s) province • year Sample Characteristic - Location China Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.15362565

The slacks-based measure (SBM) model based on the constant returns to scale has achieved some good results in addressing the undesirable outputs, such as waste water and water gas, in measuring environmental efficiency. However, the traditional SBM model cannot deal with the scenario in which desirable outputs are constant. Based on the axiomatic theory of productivity, this paper carries out a systematic research on the SBM model considering undesirable outputs, and further expands the SBM model from the perspective of network analysis. The new model can not only perform efficiency evaluation considering undesirable outputs, but also calculate desirable and undesirable outputs separately. The latter advantage successfully solves the \"dependence\" problem of outputs, that is, we can not increase the desirable outputs without producing any undesirable outputs. The following illustration shows that the efficiency values obtained by two-stage approach are smaller than those obtained by the traditional SBM model. Our approach provides a more profound analysis on how to improve environmental efficiency of the decision making units.

Air pollution control is crucial for promoting the modernization of governance systems and efficiency. To address the subjective contrived factors and errors in the gross domestic product (GDP) data in traditional statistical almanacs, our study aims to construct a panel data model of 287 prefecture-level cities for the period from 1998–2016 (using objective nighttime light data). We also used government work report words related to environmental regulation to characterize the constraints of government environmental regulations. For this purpose, we used instrumental variables (to explore the relationship and interaction between air pollution and economic growth) and a model setting, with which we carried out regression analysis and robustness tests; the findings were validated using a transmission mechanism hypothesis. We found that that economic growth and air pollution positively influence each other and government environmental regulations significantly reduce air pollution. We also found that to achieve high economic development, environmental pollution must be controlled to avoid further damage to human and material capital. Furthermore, government environmental regulations can help improve the environmental comfort level and economic development quality. First published online 7 June 2021

Purpose Technical progress is an important technique within improving China’s comparative advantages, as new and renewable technologies will be beneficial for energy security. Productive technical progress and green technical innovation are necessary to improve working conditions and productivity of industries. Therefore, the purpose of this paper is to study technical progress in China under such harsh competitive circumstances, as well as types of technical progress that can be promoted, productive technical progress or green technology progress, and how technical progress will affect China’s competitive advantages. Design/methodology/approach The authors perform a multi-index multi-factor constitutive model based on a sample of 468 Chinese industries, and divide the industries into four categories. Findings The results indicate that there is a “U”-shape relationship between green technology progress and comparative advantages and an inverted “U”-shape relationship between the intensity of market competition and comparative advantages. Research limitations/implications China has crossed the inflection point of the “U”-shaped curve. This, coupled with the slowing of economic growth, demonstrates the need for advocating green technology in China to decrease the pollutant discharge. Establishing Chinese national brands within overseas markets and earning a profit through the downstream of production chain enhance China’s international competitiveness. Originality/value One of the most original findings of this paper points out that China is faced with a situation in which exports are severely decreased and domestic environment pollution is increased. Vigorous promotion of green technology progress, improvement of the quality and the technical content of exported products, the establishment of national brand within the overseas market, as well as enhancement of China’s international competitiveness, is needed.

The Industry 4.0 is important for China to achieve industrial upgrading and promoting the quality of manufacturing development. This paper investigates the driving force of the Industry 4.0 in China’s manufacture industry, and evaluates the impact of Industry 4.0 on firm’s performance. First, a textual mining is conducted to identify 460 companies that are implementing Industry 4.0 strategy, and then a Probit model is adopted to examine the driving forces of Industry 4.0. Through the propensity scores matching difference-in-difference method, the impacts of Industry 4.0 on firm’s performance are evaluated. The results reveal that private and large companies show a higher motivation to promote the Industry 4.0 strategy, and government subsidies have no significant impact on firm’s Industry 4.0 decision. The implementation of Industry 4.0 can significant improves firm’s financial performance, innovation activities and stock returns, but has no significant impact on supply chain efficiency. In addition, the adoption of Industry 4.0 has positive impact on firm’s information transparency grade.