The Consumption-Based Carbon Emissions in the Jing-Jin-Ji Urban Agglomeration Over China's Economic Transition

Abstract Since the 2008 financial crisis, China has been undergoing an economic transition consisting of prioritizing green economic and sustainable development instead of rapid growth driven by large‐scale investment. However, there is still a lack of fine print on how subregional effort can contribute to national or full supply chain mitigation plans, especially downscaling to the city level. To bridge this knowledge gap, we selected Jing‐Jin‐Ji urban agglomeration, one of the economic centers but also featured by intensive emission for decades, to analyze the emission variance and driving forces from 2012 to 2015 as a case study. Based on the consumption accounting framework, the carbon emissions of Jing‐Jin‐Ji have decreased by 11.7 Mt CO2 in total over the study period, and most cities showed the similar descending trend. The driving forces show that the emission intensity and production structure have largely reduced Jing‐Jin‐Ji's total due to measurements of economic transition. For instance, Beijing has decreased by 28.7 Mt of emission reduction which led by declined emission intensity. By contrast, per capita demands and growth of its population were the primary forces to increase emissions. To conclude, although the mitigation achievement is undeniable, we should also note that the economic transition has not changed the uneven pattern of selected urban agglomeration so far

The Consumption-Based Carbon Emissions in the Jing-Jin-Ji Urban Agglomeration Over China's Economic Transition

Since the 2008 financial crisis, China has been undergoing an economic transition consisting of prioritizing green economic and sustainable development instead of rapid growth driven by large-scale investment. However, there is still a lack of fine print on how subregional effort can contribute to national or full supply chain mitigation plans, especially downscaling to the city level. To bridge this knowledge gap, we selected Jing-Jin-Ji urban agglomeration, one of the economic centers but also featured by intensive emission for decades, to analyze the emission variance and driving forces from 2012 to 2015 as a case study. Based on the consumption accounting framework, the carbon emissions of Jing-Jin-Ji have decreased by 11.7 Mt CO2 in total over the study period, and most cities showed the similar descending trend. The driving forces show that the emission intensity and production structure have largely reduced Jing-Jin-Ji's total due to measurements of economic transition. For instance, Beijing has decreased by 28.7 Mt of emission reduction which led by declined emission intensity. By contrast, per capita demands and growth of its population were the primary forces to increase emissions. To conclude, although the mitigation achievement is undeniable, we should also note that the economic transition has not changed the uneven pattern of selected urban agglomeration so far

Design and Implementation of the Remote Operation and Maintenance Platform for the Combine Harvester

To meet the needs of remotely operating and maintaining combine harvesters in view of the problems of extensive operation, inefficient operation and maintenance, and backward management of combine harvesters, a remote operation and maintenance platform for the combine harvester has been designed. The platform mainly includes modules, such as data monitoring, fault prediction, fault diagnosis, and comprehensive operation maintenance. Among them, the data-monitoring module obtains the monitoring data of the vehicle terminal. It actively transmits to the remote operation and maintenance platform to remotely monitor the operation data or status of the combine harvester system. The fault prediction module builds a fault prediction model based on the fault features and data provided by all previous stages. It predicts and evaluates the actual operation of each component of the combine harvester in the foreseeable future, Based on the fault diagnosis model, the fault diagnosis module evaluates and classifies the current abnormal state of the combine harvester. The comprehensive operation and maintenance module formulates the corresponding maintenance and repair strategies according to the output results of the fault diagnosis and prediction. Through functional field testing of the platform, the results showed that the platform could perform real-time monitoring, fault prediction, fault diagnosis, and comprehensive operation maintenance for the operation information of the combine harvester. It helped to improve the intelligent operation level, management quality and operation, and maintenance efficiency of the combine harvester

Design and Implementation of the Remote Operation and Maintenance Platform for the Combine Harvester

To meet the needs of remotely operating and maintaining combine harvesters in view of the problems of extensive operation, inefficient operation and maintenance, and backward management of combine harvesters, a remote operation and maintenance platform for the combine harvester has been designed. The platform mainly includes modules, such as data monitoring, fault prediction, fault diagnosis, and comprehensive operation maintenance. Among them, the data-monitoring module obtains the monitoring data of the vehicle terminal. It actively transmits to the remote operation and maintenance platform to remotely monitor the operation data or status of the combine harvester system. The fault prediction module builds a fault prediction model based on the fault features and data provided by all previous stages. It predicts and evaluates the actual operation of each component of the combine harvester in the foreseeable future, Based on the fault diagnosis model, the fault diagnosis module evaluates and classifies the current abnormal state of the combine harvester. The comprehensive operation and maintenance module formulates the corresponding maintenance and repair strategies according to the output results of the fault diagnosis and prediction. Through functional field testing of the platform, the results showed that the platform could perform real-time monitoring, fault prediction, fault diagnosis, and comprehensive operation maintenance for the operation information of the combine harvester. It helped to improve the intelligent operation level, management quality and operation, and maintenance efficiency of the combine harvester