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Abstract Rapid economic growth and accelerating urbanization in the past three decades have accelerated the exhaustion of China’s mineral resources. China is the world’s largest consumer and importer of nickel resources; therefore, a growing domestic demand will increase China’s import dependence and in turn make it potentially vulnerable to supply shortages. One hundred years from 1950 to 2050 were examined for China’s nickel utilization. Identified domestic nickel resources can only sustain China’s industries until 2017, but nickel will reach peak utilization around the year of 2020–2022. Given the 5% annual increase in applications and the growing importation of minerals in China, the carrying duration of nickel resources until 2020 will require a nickel-recycling rate of more than 90%. To sustain China’s nickel utilization, future strategies should foster three solutions: maintaining a high level of imports, adjusting the landscape of nickel applications, and shifting from virgin mining of geological minerals to urban mining of anthropogenic resources.

This research examines the role of poverty and logistical operations under the circumstance of environmental deterioration with panel data of ASEAN states from 2007 to 2017. The system-generalized method of moments (GMM) was adopted due to the presence of endogeneity. The results indicate that poverty and logistical operations have significant and positive relationship with greater environmental degradation. Because poor people are not skilled, they have to consume natural resources in original and unsustainable way for their survival and profits, which results in greater level of deforestation. On another hand, lacking fuel-efficient/green vehicles and green practices in logistical operations of ASEAN countries, logistics activities mainly depend on fossil fuel consumption, which generates greater carbon emission, methane, and greenhouse emissions that can directly damage the environment and become a primary source of climate change. Therefore, reduction in environmental degradation can be achieved through reduction in poverty and encouraging renewable energy and green practices in logistical operations. In addition, this study also provides detailed policy implications to regulatory bodies and corporate sector in order to improve environmental sustainability through adoption of green practices and reduction in poverty.

From 2006 to 2009, the aggregate energy intensity of China fell by 14.38%,1) which means that China has made substantial progress towards its goal of achieving approximately a 20% reduction in energy intensity during the period of the 11th FYP (Five-Year Plan). This paper describes some new properties of the mechanism for the decrease in China’s energy intensity since the beginning of the 11th FYP. First, compared with the period of the 10th FYP, technical, structural, and residential energy-savings have been improved to a certain extent. Secondly, unlike the dynamic mechanism before 2000, technical energy savings have been the main driver of momentum since the beginning of the 11th FYP. Next, although structural energy savings are not the main factor, they are still a key factor for the decrease in energy intensity. Finally, residential energy savings are still a stable driver of momentum for the decline in China’s energy intensity.

PurposeThe purpose of this paper is to analyze the effects of investment growth in energy sectors of western areas of China on the local economy and emission of carbon dioxide (CO2).Design/methodology/approachThe paper is based on a two‐region, ten‐sector recursive dynamic computable general equilibrium model which is based on the input‐output table of Shaanxi Province for 2002.FindingsThree different scenarios are considered in a static analysis where the investment in the energy sector of western areas is increased at the rate of 20, 40 and 60 per cent. The changes of some macro‐economic variables are simulated under these scenarios. The long‐term effects on GDP and emission of CO2 in different scenarios are also simulated. The results show that the GDP growth is 0‐8.92 per cent, households disposable income growth is 0‐8.94 per cent, and emission of CO2 growth is 0‐11.10 per cent when the investment grows between 0 and 60 per cent. The oil and gas sector is the most effective sector whose growth rate is 0‐19.47 per cent. The effects of investment growth in base period are relatively big but weaken gradually over time in the long‐run.Originality/valueThe paper is of value in highlighting the importance (for policy makers) of considering the development and application of low‐emission technologies to prevent further environmental degradation.

Operation scheduling for a class of production systems with “instantly consumed” products is very important. It is challenging to satisfy the real time system demand and to consider the realizability of the production schedules. This paper formulates a new model for optimization based production scheduling problems with integral constraints. Based on the detailed analysis of the production rate constraints, it is proved that this type of optimization problems is equivalent to a smooth nonlinear programming problem. The reachable upper and lower bounds of the production amount in every period can be expressed as functions of two variables, i.e., the production rate at the start and end of that period. It is also proved that the gradients of these functions are monotonic, and their convexity or concavity is guaranteed. When the production cost function is convex, this type of optimization problems is equivalent to a convex programming problem. With the above analysis, a two-stage solution method is developed to solve the production scheduling problems with integral constraints, and in many applications the global optimal solution can be obtained efficiently. With the new model and solution method, the difficulties caused by the constraints on production rate can be overcome and the optimal schedule can be obtained with the real time system demand satisfied. Numerical testing for scheduling of electric power production systems is performed and the testing results are discussed. It is demonstrated that the new model and solution method are effective.

Global climate change mitigation needs all countries’ efforts under the United Nations Framework Convention on Climate Change’s guideline of equity and common but differentiated responsibilities and respective capabilities. The medium-to-long term regional emissions pathways simulated by integrated assessment models with global mitigation costs minimized to achieve the 2 °C goal might be very different from the regional emissions allowances allocated based on effort-sharing principles. Global carbon trading is a cost-effective mechanism to bridge the gap. Insight of previous papers has mainly focused on the impact of a single effort-sharing scheme on global carbon market, while this study attempts to explore the scale and benefit of global carbon market under different effort-sharing principles to achieve the 2 °C goal, with the application of a consistent modeling framework, consisting of an integrated assessment model and an effort-sharing platform. The results indicate that scale of global carbon market would be highly related with the effort-sharing principles. The global trading volumes would change from 1.8 Gigatons (Gt) carbon dioxide (CO2) to over 12 GtCO2 per year and largely peak between 2030 and 2040 under different kinds of effort-sharing principles. Correspondingly, annual global finance flows in the carbon market would increase gradually and reach the scale of hundreds of billions United States (US) dollars since 2020. Global carbon market would lower the abatement costs of developed countries, and the overall global abatement costs would drop by 0.4–2.6% during 2011–2050. The developing countries would not only acquire revenues from global carbon trading but also be provided with an opportunity to accelerate their domestic low-carbon energy transformation, local environmental improvement, job creation, and economic development. Linking national and regional carbon markets to develop global carbon market will be critical to maximize the utility of the market mechanism.

Abstract China's carbon dioxide emissions from fossil fuel combustion had increased with an annual growth rate of 4.36% since 1980, hitting 1 GtC in 2003. The global climate change issue is becoming more and more important and hence to be the fourth challenge for China's future energy development, following energy supply shortages, energy security, and local environmental protection. This paper used three MARKAL (MARKet ALlocation) family models, that is, MARKAL, MARKAL-ED (MARKAL with elastic demand), and MARKAL-MACRO, to study China energy system's carbon mitigation strategies and corresponding impacts on the economy. The models’ structures and the economic feedback formulations used in MARKAL-MACRO and MARKAL-ED are briefly described. The endogenous demands in MARKAL-MACRO and MARKAL-ED enable them to partly satisfy carbon abatement constraints via energy service demand reductions, and the reduction levels for the 30 demand sectors from these two kinds of models for given carbon emission constraints are presented and compared. The impact of carbon mitigation on social welfare from MARKAL and MARKAL-ED, and on GDP, investment and consumption from MARKAL-MACRO are evaluated. The changes in both final and primary energy mix, changes in technology development, as well as marginal abatement costs for given carbon constraints from the three models, are analyzed.

This study provides new empirical evidence on the determinants of renewable energy consumption in the case of OECD economies over the period from 1990 to 2017. To examine the long run relationship among variables of renewable energy consumption and its determinants, this study uses the Durbin Hausman group mean cointegration test. The long-run and short-run coefficients are estimated via the cross-sectional Autoregressive Distributive Lag (CS-ARDL) method. The significant cointegration vector confirms the long-run equilibrium among the variables presented in the model. The results show that income, human capital, energy productivity, energy prices, and eco-innovation are important factors in explaining renewable energy consumption. This study adopts the Augmented Mean Group (AMG) method to check the robustness of the model. The results are found to be consistent with the estimates of the cross-sectional Autoregressive Distributive Lag Model method. To offer viable solutions to environmental problems and to achieve the targets set in the Paris Climate Agreement, policies and strategies should be devised to increase the share of renewable energy in the overall energy mix.

Abstract Cross-industry networks of multiple supply chains have evolved in the circular economy model using approaches such as industrial and urban symbiosis. However, the implementation of such sustainable industrial networks with matrix-like structures is not straightforward. Despite the clear benefits of big data-driven industrial symbiosis, corporates have noted that social, environmental and economic perspectives are also highly appreciated in the cross-industry networks. Moreover, gaps remain in operational data-driven and recycle, reduce and reuse optimization solutions, which may be the key components of industrial symbiosis practices.