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Developing a low-carbon economy is the only way for countries to achieve sustainable development. Carbon emission reduction policies and low-carbon technology (LCT) innovation play key roles in developing low-carbon economy. Under government reward and punishment regulations, based on the bilateral matching and evolutionary theories, this paper constructs an evolution model consisting of a manufacturer investing LCT and a supplier offering LCT to analyze multi-phase LCT investment strategies. Firstly, the profit optimization model of a green supply chain is constructed from the perspectives of centralized-matching (CM), decentralized-matching (DM), and mismatching (MM), and the spatial information internal evolution law of multi-phase LCT investment is described by the Markov chain. Then, a bilateral matching algorithm is proposed to solve the equilibrium solutions, and the evolution process of the three modes is analyzed by numerical simulation. Finally, based on the product green degree, we analyze the impact of subsidies and taxes on investment-production decisions. Analytical results show that the matching mechanism proposed in this paper can help supply chain firms to obtain stable matching and has a significant effect on the realization of "triple wins" of society, economy, and environment. The investment utility of CM is higher than that of DM and MM. Manufacturers are inclined to adopt LCT, and the investment level tends to be stable over time. Government reward and punishment regulations are helpful to motivate supply chain firms to invest in LCT, and the synergistic effect of subsidies and taxes is better than that of a single policy.

Abstract We present a production-theoretical approach to decomposing the change of aggregate CO2 emissions over time using the Shephard input distance functions and the environmental data envelopment analysis (DEA) technology in production theory. Application of the proposed approach requires only some aggregate data and the change of aggregate CO2 emissions can be decomposed into contributions from seven factors. These contributions are obtained through solving a series of DEA models. The key features of the proposed approach are the use of panel data to estimate the production frontier and the inclusion of several production technology related components. Using the proposed approach, we present two application studies on decomposing the CO2 emissions for world regions and OECD countries. A comparison between the proposed approach and other decomposition analysis methods is also presented.

The object of the study is the water supply and sanitation system of an industrial enterprise. Currently, many enterprises do not use the treated industrial waste water in the water supply cycle. Therefore, the use of treated wastewater is of practical importance. The scheme of thermal treatment of waste water for obtaining pure condensate by evaporation is proposed. The main results of the study are the technical solution for the development of the enterprise's wastewater treatment complex. Pure condensate is obtained in an instant boiling evaporator. The research method is based on the calculation of energy costs in wastewater treatment for different types of waste water. The results of the study allow us to determine the optimal set of energy equipment for obtaining pure condensate and its further use. The specific practical significance lies in the development of a technical solution that helps the treatment of wastewater and the reduction of emissions into the water basin.

Abstract Mitigating the effects of environmental deterioration requires a focus on not just CO2 emissions from energy consumption, but also environmental pollution from industry sectors. To reach this goal, recent studies have extended ecological footprint (EF) analysis to identify the ecological drivers of various key industry sectors. The role of the phosphorus (P) industry on the EF within the environmental Kuznets curve (EKC) framework for China is the emphasis of this study. Autoregressive distributive lag (ARDL) as well as the impulse response function and robustness analysis were used to consider a time from 1985 to 2018. The study verifies the EKC hypothesis for China in both the long and the short run, and indispensable determinants are proposed to be included to assure the model’s fitness and robustness when conducting EF analysis of industry sectors. Energy consumption–based carbon emissions have been verified as the dominant contributor to EF, but P use and urbanization have a significant lagged positive influence on EF in the short run. P exports, in particular, have been highlighted as a critical driver of the EF of China’s P industry. The conducted frequency domain causality test reinforced the above findings and demonstrated bidirectional causality at different frequencies. This work suggests that formulating plausible P export policies to alleviate the conflict between the output of China’s P industry and the environmental sustainability of this industry are necessary. In this context, “multidisciplinary, multidimensional, and practical solutions” are most desirable for sustainable P management.

As a financial innovation model to promote sustainable economic development, carbon finance promotes green and inclusive economic development by reshaping the financial system. As a big energy consumer, China has been committed to the green transformation of energy structure, and carbon finance has been regarded as an important tool to support carbon emission reduction. This paper divides carbon finance from three levels, financial macro, environmental governance, and technological development, and selects 17 indexes to measure the development level of carbon finance in China. Time series regression model and the entropy weight method (EWM) are used to analyze the impact of carbon finance on China's energy consumption structure. The empirical results show that carbon finance plays a positive role in promoting China's energy consumption structure, and developing carbon finance is conducive to the use of clean energy and the optimization of energy consumption structure in China; carbon finance can further optimize China's energy consumption structure by investing capital, resources, and technology in industries related to promoting clean energy consumption. Therefore, China should continue to develop carbon finance and promote the establishment of a unified national carbon trading market; at the same time, China should use the CCER trading mechanism to promote the development of renewable energy and enhance the endogenous driving force of energy transformation by promoting technological progress.

Abstract Global environmental change is driven by food production. Biogas from food waste is a better source of clean energy. Ghana’s energy strategy targets a 10% increase in renewable energy and modern biomass in the national electricity generation mix. Studies on the assessment of electricity generation potential and economic feasibility of biogas to electricity projects in Ghana’s major cities are scarcely available. This study assesses the electricity generation potential of biogas from food waste through anaerobic digestion technology. The municipal solid waste generation potential of Accra and Kumasi was estimated from 2020 to 2039. The potential theoretical methane yield from food waste was calculated using Buswell’s equation. The study analyzed anaerobic digestion projects’ economic feasibility using the total life cycle cost, net present value, investment payback period, levelized cost of energy, and internal rate of return methods. A sensitivity analysis based on two scenarios (optimistic and pessimistic) was performed to analyze the influence of changes in the composition of food waste, per capita waste generation rate, population growth rate, per capita GDP growth rate, discount rate, capacity factor, electricity generation efficiency, waste collection efficiency, and methane production potential on the economic feasibility of the projects. The main findings indicate that the amount of waste generation in Accra during the project life cycle is 899,000 t/y to 3,359,000 t/y, while that of Kumasi is 915,000 t/y to 3,159,000 t/y. The power generation potential of the project in Accra ranges from 80.43 to 300.49 GWh/y, and in Kumasi ranges from 60.63 to 209.31 GWh/y. Economically, the project is feasible in Accra and Kumasi. The net present value of the project in Accra and Kumasi is $217,800,000 and $156,100,000. The sensitivity analysis shows that the project is infeasible in all the cities with a discount rate exceeding 20%. When the discount rate exceeds 20%, the project becomes highly infeasible in Accra compared to Kumasi. This study will offer itself as scientific guidance for investment in biogas to electricity projects in Ghana’s cities.

Abstract Since energy consumption (EC) is becoming an important issue for sustainable development in the world, it has a practical significance to predict EC effectively. However, there are two main uncertainty factors affecting the accuracy of a region's EC prediction. Firstly, with the ongoing rapid changes in society, the consumption amounts can be non-smooth or even fluctuating during a long time period, which makes it difficult to investigate the sequence's trend in order to forecast. Secondly, in a given region, it is difficult to express the consumption amount as a real number, as there are different development levels in the region, which would be more suitably described as interval numbers. Most traditional prediction models for energy consumption forecasting deal with long-term real numbers. It is seldom found to discover research that focuses specifically on uncertain EC data. To this end, a novel energy consumption forecasting model has been established by expressing ECs in a region as interval grey numbers combining with the optimized discrete grey model (DGM(1,1)) in Grey System Theory (GST). To prove the effectiveness of the method, per capita annual electricity consumption in southern Jiangsu of China is selected as an example. The results show that the proposed model reveals the best accuracy for the short data sequences (the average fitting error is only 2.19% and the average three-step forecasting error is less than 4%) compared with three GM models and four classical statistical models. By extension, any fields of EC, such as petroleum consumption, natural gas consumption, can also be predicted using this novel model. As the sustained growth in EC of China's, it is of great significance to predict EC accurately to manage serious energy security and environmental pollution problems, as well as formulating relevant energy policies by the government.

In the past 10 years, the implementation of grassland ecological compensation policy has played an important role in the sustainable development of the pastoral economy. How much impact on herders will delineating the ecological conservation redline have? Such delineation is significant for the smooth implementation of the ecological conservation redline. Based on this, taking the three banner counties with a large area under the control area of the ecological redline of the Xilin Gol as examples, OLS and quantile regression were used to analyze the impact of a grassland ecological compensation policy on herdsmen’s income level, and the ordered Probit model was used to analyze the influencing factors of herdsmen’s satisfaction with the policy. The results show that: (1) grassland ecological compensation has a significant positive impact on low-income herders in ecological protection redline areas; (2) grassland ecological compensation, income and the implementation of current policy have a positive impact on the satisfaction of herders in the redline area; (3) herdsmen are highly satisfied with the grassland ecological compensation policy, but there is still a lot of room for improvement in the compensation policy after the redline is delineated. In this regard, we should increase compensation for areas with a high proportion of ecological conservation redlines, and explore ways to increase income from animal husbandry products. At the same time, we should strengthen the publicity of ecological protection redline policies and promote the timely disbursement of funds, reconstruct the grassland ecological compensation mechanism by strengthening policy incentives and hardening regulatory constraints, and effectively improve the policy efficiency of ecological protection redlines.

Agro-wastes, such as crop residues, leaf litter, and sawdust, are major contributors to global greenhouse gas emissions, and consequently a major concern for climate change. Nowadays, mushroom cultivation has appeared as an emerging agribusiness that helps in the sustainable management of agro-wastes. However, partial utilization of agro-wastes by mushrooms results in the generation of a significant quantity of spent mushroom substrates (SMS) that have continued to become an environmental problem. In particular, Shiitake (Lentinula edodes Berk.) mushrooms can be grown on different types of agro-wastes and also generate a considerable amount of SMS. Therefore, this study investigates the biotransformation of SMS obtained after Shiitake mushroom cultivation into biogas and attendant utilization of slurry digestate (SD) in tomato (Solanum lycopersicum L.) crop fertilization. Biogas production experiments were conducted anaerobically using four treatments of SMS, i.e., 0% (control), 25, 50, and 75% inoculated with a proportional amount of cow dung (CD) as inoculum. The results on biogas production revealed that SMS 50% treatment yielded the highest biogas volume (8834 mL or 11.93 mL/g of organic carbon) and methane contents (61%) along with maximum reduction of physicochemical and proximate parameters of slurry. Furthermore, the biogas digestate from 50% treatment further helped to increase the seed germination (93.25%), seedling length (9.2 cm), seedling root length (4.19 cm), plant height (53.10 cm), chlorophyll content (3.38 mg/g), total yield (1.86 kg/plant), flavonoids (5.06 mg/g), phenolics (2.78 mg/g), and tannin (3.40 mg/g) contents of tomato significantly (p < 0.05) in the 10% loading rate. The findings of this study suggest sustainable upcycling of SMS inspired by a circular economy approach through synergistic production of bioenergy and secondary fruit crops, which could potentially contribute to minimize the carbon footprints of the mushroom production sector.

Achieving carbon-neutrality has become a global agenda following the ratification of the Paris Agreement. For the developing countries, in particular, attaining a low-carbon economy is particularly important since these economies are predominantly fossil-fuel dependent, to which Bangladesh is no exception. Therefore, this study specifically aimed at evaluating the environmental impacts associated with energy consumption and other key macroeconomic variables in the context of Bangladesh over the 1975-2016 period. As opposed to the conventional practice of using carbon dioxide emissions to proxy environmental quality, this study makes a novel attempt to use the carbon footprints to measure environmental welfare in Bangldesh. The outcomes from this study are expected to facilitate the carbon-neutrality objective of Bangladesh and, therefore, enable the nation to comply with its commitments concerning the attainment of the targets enlisted under the Paris Agreement and the United Nations Sustainable Development Goals declarations. The econometric analysis involved the application of methods that are suitable for handling the structural break issues in the data. The overall findings from empirical exercises reveal that aggregate energy consumption, fossil fuel consumption, and natural gas consumption boost the carbon footprint figures of Bangladesh. In contrast, nonfossil fuel consumption and hydroelectricity consumption are witnessed to abate the carbon footprint levels. Besides, economic growth and international trade are also evidenced to further increase the carbon footprints. Hence, these findings suggest that a clean energy transition within the Bangladesh economy can be the panacea to the nation's persitently aggravating environmental hardships. Furthermore, the causality analysis confirmed the presence of unidirectional causalities stemming from total energy consumption, fossil fuel consumption, natural gas consumption, hydroelectricity consumption, economic growth, and international trade to the carbon footprints. On the other hand, nonfossil fuel consumption is found to be bidirectionally associated with carbon footprints. In line with these aforementioned findings, several key policy suggestions are put forward regarding the facilitation of the carbon-neutrality agenda in Bangladesh.