This study first attempts to use the parameterized quadratic Directional Distance Function (DDF) approach to calculate China's provincial carbon abatement cost and carbon reduction potential (CRP) under different scenarios from 2000 to 2017. Afterward, considering three different scenarios, we analyze the Spatio-temporal characteristics and the dynamic evolution pattern of CRP. We also conduct the spatial autocorrelation test and spatial Durbin model to analyze the spatial spillover effects and influencing factors of CRP. The results are obtained as follows: CRP across the three scenarios varies considerably across provinces and different-located groups. CRP higher areas are mainly located in the economically developed eastern coastal regions, while most provinces with low CRP are concentrated in the western region. The spatial autocorrelation test indicated that provinces with a similar CRP showed a significant geographic agglomeration, and the agglomeration effect was strengthened first and then weakened. Simultaneously, the local spatial distribution of MCRP, FCRP, and ECRP shows a slight spatial polarization feature. Finally, through the SDM analysis and spillover effect decomposition, we find that improvement of regional CRP not only depends on economic development, industrial structure adjustment, and energy efficiency elevation, but also involves energy structure optimization, low-carbon innovation, and population. The low-carbon innovation provides critical support for local CRP under the efficiency scenario but restrains the local CRP under the fairness scenario. Therefore, the central government should emphasize local conditions and the ex-ante scenario assessment, strengthen regional interactive governance, optimize energy efficiency, and promote the application of clean energy to enhance CRP.

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This study first attempts to use the parameterized quadratic Directional Distance Function (DDF) approach to calculate China's provincial carbon abatement cost and carbon reduction potential (CRP) under different scenarios from 2000 to 2017. Afterward, considering three different scenarios, we analyze the Spatio-temporal characteristics and the dynamic evolution pattern of CRP. We also conduct the spatial autocorrelation test and spatial Durbin model to analyze the spatial spillover effects and influencing factors of CRP. The results are obtained as follows: CRP across the three scenarios varies considerably across provinces and different-located groups. CRP higher areas are mainly located in the economically developed eastern coastal regions, while most provinces with low CRP are concentrated in the western region. The spatial autocorrelation test indicated that provinces with a similar CRP showed a significant geographic agglomeration, and the agglomeration effect was strengthened first and then weakened. Simultaneously, the local spatial distribution of MCRP, FCRP, and ECRP shows a slight spatial polarization feature. Finally, through the SDM analysis and spillover effect decomposition, we find that improvement of regional CRP not only depends on economic development, industrial structure adjustment, and energy efficiency elevation, but also involves energy structure optimization, low-carbon innovation, and population. The low-carbon innovation provides critical support for local CRP under the efficiency scenario but restrains the local CRP under the fairness scenario. Therefore, the central government should emphasize local conditions and the ex-ante scenario assessment, strengthen regional interactive governance, optimize energy efficiency, and promote the application of clean energy to enhance CRP.

Low-carbon technology innovation plays an essential role in carbon emission reduction worldwide. This study investigates how low-carbon innovation affects carbon emissions by the Dynamic Spatial Durbin Model based on the panel data of 30 Chinses provinces from 2007 to 2017. The empirical results show that: Firstly, low-carbon innovation decreases carbon emissions from local and neighbor, the decreasing effects are significant mainly in the short term. Secondly, the results of the heterogeneity test indicate that the weakening effect of low-carbon innovation in central regions is consistent with the national results. The weakening effects are shown in long-term indirect and short-term direct in eastern regions. Thirdly, there is an inverted-U curve between economic development and carbon emissions, confirming the environmental Kuznets curve (EKC) hypothesis. However, the inflection point is insurmountable under the current level of technology in China. Finally, The results also show the “Pollution Paradise” effect.

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This paper introduces the basic supply chain management of apparel industry, analyses its application commercial value of RFID in the apparel industry, and studies RFID technology in the process of apparel supply management. Through the establishment of RFID-based apparel supply chain logistics information environment, the apparel enterprises can realize the apparel supply chain Logistics Information automation, real-time, accurate collection and supply chain logistics of comprehensive tracking management and tracing, thereby improve the management level of enterprises. This paper puts forward solutions and system designs to the implementation of apparel manufacturing supply chain management, which has great enlightenment effect. The successful application of the scheme will bring about revolutionary reform to apparel manufacturing, processing and circulation of supply chain management.

Based on the panel data in Chinese provinces from 2000 to 2017, this paper first uses the parameterized quadratic function of the directional distance function to estimate carbon abatement costs of 30 provinces in China, and further studies its long-term evolutionary characteristics. Second, this paper studies the spatial distribution pattern of carbon abatement cost. The results show that the carbon abatement cost has increased as a whole during the study period. Moreover, the spatial distribution of carbon abatement costs in China shows a geographical clustering feature, and the positive spatial agglomeration is significant after 2008.