Carbon emission quota allocation of high energy consumption industries in undeveloped areas – A case study of Inner Mongolia Autonomous Region

Zhang, Xiufan; Fan, Deng-Ping

doi:10.1016/j.heliyon.2022.e11241

Cited by 7 publications

(3 citation statements)

References 43 publications

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“…The multi-criteria used in this study uses sustainability parameters so that the bioenergy agroindustry can be competitive sustainably. The weighting to classify these parameters using the Analytic Hierarchy Process (AHP), which is recognized to work together with GIS procedures to determine the best location [23]. Combining these approaches is expected to facilitate the modeling of bioenergy agroindustry site selection [24,25].…”

Section: Table 2 Index Random Consistencymentioning

confidence: 99%

Decision Support System for Bioenergy Supply Chain Optimization: A Case Study at Lebak District, Banten Indonesia

Krisnaningsih,

Arkeman,

Marimin

et al. 2024

IJSDP

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This study aims to design a decision support system (DSS) development model in sustainable bioenergy supply chains. Our approach involves 1) identifying the bioenergy supply chain model through determining the spatial potential model of agroindustry area using geographic information system (GIS) and analytical hierarchy process (AHP); 2) determining the optimization model for aggregate production process planning using fuzzy goal programming for bioenergy production to design the biomass inventory level determination model using adaptive neuro-fuzzy inference system (ANFIS) approach; 3) designing the concept of DSS model in bioenergy supply chain. The results showed the identification of the supply chain model from the spatial model of potential agroindustry locations with three regional categories: 19.34% potential, 16.93% not potential, and 63.70% developing. Aggregate planning is appropriate based on three objective functions to be achieved in production planning for determining inventory levels using ANFIS using three input variables and comparing performance with RMSE, MAPE, and R2 inventory levels so that the model can predict inventory levels adaptively. The concept of the DSS Model on the bioenergy supply chain from agricultural centers to users by adding Internet of Things (IoT) technology can increase the effectiveness and efficiency of the bioenergy supply chain. The managerial implications of this research can provide relevant insights for the design and improvement of renewable energy management programs. Utilization of local biomass resources becomes more optimal.

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Section: Table 2 Index Random Consistencymentioning

confidence: 99%

Decision Support System for Bioenergy Supply Chain Optimization: A Case Study at Lebak District, Banten Indonesia

Krisnaningsih,

Arkeman,

Marimin

et al. 2024

IJSDP

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“…From 2006 to 2020, the scale of the coal mining and washing industry decreased from RMB 7.51 to 0.5 million in Zhejiang province, from RMB 1.234 billion to RMB 0.107 million in Hubei province, and decreased from RMB 2.449 billion to RMB 0.963 million in Tianjin. From the areas rich in high-carbon energy industries, most of them are the central and western regions, whose economic development is highly dependent on energy, with a low-energy utilization rate [35], relatively extensive urban land expansion, and the transfer of several high-carbon industries in the eastern region, and many high-carbon enterprises. Existing research results show that there is a close relationship between high-carbon industries and carbon emissions, which are the main source of carbon emissions in China.…”

Section: Heterogeneity Analysismentioning

confidence: 99%

Effect of Scale and Structure Changes of China’s High-Carbon Industries on Regional Carbon Emissions

Liang,

Pan

2023

Energies

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China’s carbon emissions have a stable industrial concentration. In recent years, the carbon emissions of the six major high-carbon industries have accounted for approximately 80% of the national total and are thus priority areas for emission reduction. With the promotion of energy-saving and emission-reduction policies, the structure and scale of high-carbon industries in various regions have undergone changes, but their carbon reduction effects show significant regional differences. Based on China’s provincial panel data from 2006 to 2020, this study discusses the structural characteristics of high-carbon industries with their proportion of energy-based industries and measures their scale characteristics with their output values. On this basis, a fixed-effects model is used to analyze the single and synergistic effects of the scale and structure of high-carbon industries on carbon emissions in each province. The results indicate that changes in the scale and structure of high-carbon industries significantly affect carbon emissions but show regional differences in both the single and synergistic effects. When considering these synergistic effects, the single effect of high-carbon industries on carbon emissions will be weakened. In regions with large-scale high-carbon industries, the increase in the proportion of energy-based industries significantly increases carbon emissions, but this effect gradually weakens as the overall scale expands. In areas with small-scale high-carbon industries, the increase in the proportion of energy-based industries has a relatively small effect on carbon emission growth that gradually increases with the overall scale. In addition, the implementation of the carbon emission trading policy has a significant moderating effect on the carbon emissions of high-carbon industries and strongly promotes its reduction.

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“…It is important and fruitful to methodically analyze and consider the findings that have already been achieved in this subject as theoretical research and practical experience have accumulated [ 6 ]. Some overviews of carbon neutrality from specialized perspectives have previously been presented by researchers from related fields in several research areas, including studies on the use of environmentally friendly materials [ 7 ], difficulties in high carbon emission industries [ 8 , 9 ], carbon-neutral technologies [ 10 , 11 ], and energy transition pathways [ 12 ], etc. The field of carbon neutrality is advanced by these publications, which offer a thorough summary of the disciplinary knowledge within it.…”

Section: Introductionmentioning

confidence: 99%

A bibliometric analysis of carbon neutrality: Research hotspots and future directions

Gou

Zhang

et al. 2023

Heliyon

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Carbon emission quota allocation of high energy consumption industries in undeveloped areas – A case study of Inner Mongolia Autonomous Region

Cited by 7 publications

References 43 publications

Decision Support System for Bioenergy Supply Chain Optimization: A Case Study at Lebak District, Banten Indonesia

Decision Support System for Bioenergy Supply Chain Optimization: A Case Study at Lebak District, Banten Indonesia

Effect of Scale and Structure Changes of China’s High-Carbon Industries on Regional Carbon Emissions

A bibliometric analysis of carbon neutrality: Research hotspots and future directions

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