In this paper, we systematically explore the environmental effects of the export tax rebate rate reduction policy using the China Industrial Enterprise Database, the China Industrial Enterprise Pollution Database, and the China Customs Import and Export Database from 2005 to 2013. Our difference-in-difference (DID) estimates show that the reduction in the export tax rebate rate significantly reduces the intensity of corporate soot emissions, and this finding holds after a series of robustness tests. For every 1-unit reduction in export tax rebate rate, industrial exporters’ soot emission intensity decreases by 2.63%. The mechanism analysis shows that the decrease in soot generation, the decrease in coal use intensity, the increase in total amount and efficiency of soot treatment are important channels. Heterogeneity analysis shows that the reduction of export tax rebate rate has a more significant impact on the intensity of soot emissions of high pollution, high energy consumption and resource-based enterprises. This study may provide a reference for other developing countries that also rely on export tax rebates to adjust their policies to combine economic growth with pollution control.

Cold chain logistic firms have been motivated to decrease overall operating costs and carbon emissions to capture economic edge and maintain profitability by intense competition and financial energy requirements. C Our analysis develops a model cold-chain logistic and distribution system (CC-LDS) for logistics and transport firms working together to manufacture chilled and frozen goods by introducing carbon tax policies. The CC-LDS model provides a logistics and transport network. Virtual annealing (VA) algorithm for optimising the model is implemented based on actual customer information from multiple cold storage firms and 30 clients. The findings suggest that the second derivative is optimal compared to the individual distribution to slash overall expense and carbon pollution. The net cost is strongly associated with the cost of carbon, and energy consumption is similar to the price of carbon grows. Moreover, carbon caps have little effect on the direction of distribution.To best leverage social and technological capital to accomplish equal financial and ecological gains.

Supply chain management has become increasingly important as an academic subject due to globalization developments contributing to massive production-related benefits reallocation. The huge volume of data produced in the global economy means that new tools must be created to manage and evaluate the data and measure organizational performance worldwide. Smart technologies such as swarm intelligence and big data analytics can help get clear data of the location, condition, and environment of products and processes at any time, anywhere to make smart decisions and take corrective schedules that the supply chain can run more effectively. This study proposes the swarm intelligence modeling-based logistic analytics management (SIMLAM) in service supply chain market. A generalized structure for swarm intelligence implementation in supply chain management is suggested, which is advantageous to industry practitioners. Different deterministic methods practically fail due to the intrinsic computational complexity of the problem of higher dimensions.

The optimal productivity model plays a significant role in various supply chain management (SCM) decision support systems. Therefore, the precision of the optimal productivity model is necessary to improve SCM's effectiveness. A factor often ignored is that transactions of certain goods are assembled within an enterprise as dynamic structures of various distribution ratios. Regardless of such structure, optimal model productivity is often produced; however, the productivity model's optimal precision can be enhanced by taking it into account. This focusses on strategic thinking and planning, where various process improvement mechanisms are developed. Therefore, in this study, data envelopment analysis (DEA) has been utilized to enhance supply chain efficiency and effectiveness management. This paper explores an optimal productivity model that evaluates the supply chain efficiency and effectiveness management. This paper discusses the policy preparation demands of the decision support systems and develops a framework that organisations can use to control the implementation process.