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Recycling plastic can abate the environmental pollution as well as CO2 emissions by saving the carbon-intensive feedstock input. The uncertain carbon price places significant effects on the establishment and operation of the whole supply chain. This study develops a green supply chain model combined with geographic information system (GIS) to account for carbon price uncertainty and evaluate its effects on the closed-loop supply chain (CLSC) of plastic recycling. A two-stage stochastic programming model is constructed, in which the stochastic variable, CO2 price is modelled as a geometric Brownian motion process. Six scenarios are designed with respect to price expectation and volatility. A case study is performed with the GIS information of the plastic supply chain in Zhejiang province, China. The results illustrate that triggering the establishment of reverse logistics requires a carbon price threshold significantly beyond the current level. Lower price volatility would facilitate the decision-making of investment into the reverse logistics. Mechanisms to alleviate the market variation shall be introduced. A sound market condition is desired to obtain the optimal balance that encourages the CLSC without creating extra pressure on the firms. The proposed modelling framework can be easily applied to other sectors with similar characteristics. [ABSTRACT FROM AUTHOR]

Promoting energy efficiency in iron and steel production provides opportunities for mitigating environmental impacts from this energy-intensive industry. Energy efficiency technologies differ in investment costs, fuel-saving potentials, and environmental performance. Hence the decision-making of the adoption strategy needs to prioritize technological combinations concerning these multi-dimensional objectives. To address this problem, this study proposes a hybrid multi-criteria decision-support model for adopting energy efficiency technologies in the iron and steel industry. The modeling framework integrates a linear programming model that determines the optimal technology adoption rates based on the techno-economic, energy, and environmental performance details and an interactive multi-criteria model analysis tool for diverse modeling environments. A real case study was performed in which a total number of 56 energy efficiency technologies were investigated against various criteria concerning economics, energy, and environmental performances. The results examine the tradeoffs and synergies were examined with regard to seven criteria. A balanced solution shows that a total investment of 13.4 billion USD could save 2.51 Exajoule fuel consumption, cut 67.4 million tons (Mton) CO2 emissions, and reduce air pollution of 1.5 Mton SO2, 1.41 Mton NOx, and 0.86 Mton PM, respectively. The case study demonstrates the effectiveness and applicability of the proposed multi-criteria decision-making support framework. [ABSTRACT FROM AUTHOR]

The industrialization process in China has resulted in the fast growth of the country's energy consumption and CO2 emissions. Examining the effects of industrial structural change on the emissions pathways in the mid-term future would help advance understanding of how industrial policy choices affect the fulfillment of the strategic climate targets of emissions peaking and carbon neutrality. This study couples index decomposition analysis (IDA) with an additive nonparametric regression model to project the possible emissions pathways with different industrial structures. It develops a set of scenarios following the storylines of shared socioeconomic pathways (SSPs) to examine these effects in an uncertain environment towards 2040. The results show that structural change has played an increasing role in curbing carbon emissions of China's industrial sectors since 2000. The CO2 emissions reductions attributable to this effect were 686 million tons (Mton CO2) between 2000 and 2013, and these contributions to emission mitigation rose to 798 Mton between 2014 and 2019. The scenario results suggest that the aggregated effect of energy efficiency and structure upgrades will decrease emissions by 43% in 2040 relative to the level in 2019 in the ideal case. Regardless of the uncertainties in scenario settings, heavy industry will continue to dominate China's industrial emissions through 2040. Nevertheless, a significant structural change with an increased share of high-tech industries, such as information and communication technology, could lead to more than a 30% reduction in emissions compared to cases with more minor changes in this sub-sector. Key Policy Insights A rapid expansion of heavy industry is the primary factor driving the rapid growth of China's industrial CO2 emissions since 2000. Industrial structural change is the second most significant factor curbing emissions growth, and the influence of this factor is increasing. Keeping up the momentum of structural change and technological upgrades in the industrial sector would make it possible for industry emissions to decrease in the medium-term future, therefore contributing substantially to China's goal of peaking its CO2 emissions by 2030. Realizing carbon neutrality in the longer term necessitates not only structural change in industry, but also a fundamental transformation of the energy supply system. [ABSTRACT FROM AUTHOR]