Does the individual effect of resource rents imperative in the attainment of environmental sustainability? Evidence of Southeast Asian economies.

This study investigates the impact of natural resource exploitation on environmental sustainability in Southeast Asian economies, while testing the Environmental Kuznets Curve (EKC) inverted U-shaped hypothesis, a model which suggests an initial increase in environmental degradation with economic growth followed by a decrease at a certain level of income. Utilizing World Development Indicators data from 1995 to 2018, the research dissects the long-term influence of various resource rents, namely coal, oil, and forest. The research highlights the indispensable role of renewable energy in maintaining ecological balance. Results indicate that while coal rent exacerbates environmental degradation, forest and oil rents prove eco-friendly, although this is only confirmed in fully modified OLS estimation. The study underscores the importance of forest rents in achieving environmental sustainability. Renewable energy emerges as vital for promoting sustainable low-carbon practices. In line with the EKC hypothesis, the study finds that economic growth initially increases carbon emissions, but eventually reduces them. It calls for appropriate measures to manage resource exploitation, ensure renewable energy availability, alleviate energy poverty, and curb deforestation, thereby mitigating ecological damage.

Energy taxes, energy innovation, and green sustainability: empirical analysis from a China perspective.

The idea that energy taxes and innovation may contribute to lowering greenhouse gas emissions and fostering the development of a more sustainable energy future is gaining popularity. Therefore, the study's main goal is to explore the asymmetric impact of energy taxes and innovation on CO2 emissions in China by employing linear and nonlinear ARDL econometric methods. The outcomes of the linear model demonstrate that long-term increases in energy taxes, energy technological innovation, and financial development cause CO2 emissions to reduce, while increases in economic development cause CO2 emissions to climb. Similarly, energy taxes and energy technological innovation cause CO2 emissions to fall in the short run, while financial development promotes CO2 emissions. On the other hand, in the nonlinear model, the positive energy changes, positive energy innovation changes, financial development, and human capital help reduce the long-run CO2 emissions, and economic development increase the CO2 emissions. In the short run, the positive energy and innovation changes are negatively and significantly connected to CO2 emissions, while financial development is positively linked to CO2 emissions. The negative energy innovation changes are insignificant in both the short and long run. Therefore, Chinese policymakers should try to promote energy taxes and innovations as tools to achieve green sustainability.

A Low-Carbon and Economic Dispatch Strategy for a Multi-Microgrid Based on a Meteorological Classification to Handle the Uncertainty of Wind Power.

In a modern power system, reducing carbon emissions has become a significant goal in mitigating the impact of global warming. Therefore, renewable energy sources, particularly wind-power generation, have been extensively implemented in the system. Despite the advantages of wind power, its uncertainty and randomness lead to critical security, stability, and economic issues in the power system. Recently, multi-microgrid systems (MMGSs) have been considered as a suitable wind-power deployment candidate. Although wind power can be efficiently utilized by MMGSs, uncertainty and randomness still have a significant impact on the dispatching and operation of the system. Therefore, to address the wind power uncertainty issue and achieve an optimal dispatching strategy for MMGSs, this paper presents an adjustable robust optimization (ARO) model based on meteorological clustering. Firstly, the maximum relevance minimum redundancy (MRMR) method and the CURE clustering algorithm are employed for meteorological classification in order to better identify wind patterns. Secondly, a conditional generative adversarial network (CGAN) is adopted to enrich the wind-power datasets with different meteorological patterns, resulting in the construction of ambiguity sets. Thirdly, the uncertainty sets that are finally employed by the ARO framework to establish a two-stage cooperative dispatching model for MMGS can be derived from the ambiguity sets. Additionally, stepped carbon trading is introduced to control the carbon emissions of MMGSs. Finally, the alternative direction method of multipliers (ADMM) and the column and constraint generation (C&CG) algorithm are adopted to achieve a decentralized solution for the dispatching model of MMGSs. Case studies indicate that the presented model has a great performance in improving the wind-power description accuracy, increasing cost efficiency, and reducing system carbon emissions. However, the case studies also report that the approach consumes a relative long running time. Therefore, in future research, the solution algorithm will be further improved for the purpose of raising the efficiency of the solution.

How does income and green technology innovation influence the emissions reduction effect of renewable energy: evidence from Chinese provincial data.

The development of renewable energy is a key measure to achieving carbon neutrality in China. Considering the significant regional differences in income levels and green technology innovation, it is essential to discuss the impact of renewable energy development on carbon emissions from the Chinese provincial level. Based on the panel data of 30 provinces in China from 1999 to 2019, this study first explores the impact of renewable energy on carbon emissions and regional heterogeneity. Moreover, the moderating effects of income levels on the nexus between renewable energy and carbon emissions, and the impact mechanism of green technology innovation are further examined. Results show that, first, renewable energy development can significantly reduce carbon emissions in China, and there exist obvious regional differences. Second, income levels present a non-linear moderating effect on the relationship between renewable energy and carbon emissions. The increase in income levels can effectively enhance the emission reduction effect of renewable energy only in high-income regions. Third, renewable energy development is an important mediating mechanism for green technology innovation to achieve emission reduction. Finally, policy implications are proposed to help China in advancing the development of renewable energy and achieve carbon neutrality.

Optimizing renewable-based energy supply options for power generation in Ethiopia.

Ethiopia unveiled homegrown economic reform agenda aimed to achieve a lower-middle status by 2030 and sustain its economic growth to achieve medium-middle and higher-middle status by 2040 and 2050 respectively. In this study, we evaluated the optimal renewable energy mix for power generation and associated investment costs for the country to progressively achieve upper-middle-income countries by 2050. Two economic scenarios: business as usual and Ethiopia's homegrown reform agenda scenario were considered. The study used an Open Source energy Modeling System. The model results suggest: if projected power demand increases as anticipated in the homegrown reform agenda scenario, Ethiopia requires to expand the installed power capacity to 31.22GW, 112.45GW and 334.27GW to cover the current unmet and achieve lower, medium and higher middle-income status by 2030, 2040 and 2050 respectively. The Ethiopian energy mix continues to be dominated by hydropower and starts gradually shifting to solar and wind energy development towards 2050 as a least-cost energy supply option. The results also indicate Ethiopia needs to invest about 70 billion US$ on power plant investments for the period 2021-2030 to achieve the lower-middle-income electricity per capita consumption target by 2030 and staggering cumulative investment in the order of 750 billion US$ from 2031 to 2050 inclusive to achieve upper-middle-income electricity consumption rates by 2050. Ethiopia has enough renewable energy potential to achieve its economic target. Investment and financial sourcing remain a priority challenge. The findings could be useful in supporting decision-making concerning socio-economic development and investment pathways in the country.

Modelling welfare estimates in discrete choice experiments for seaweed-based renewable energy.

We explore what researchers can gain or lose by using three widely used models for the analysis of discrete choice experiment data-the random parameter logit (RPL) with correlated parameters, the RPL with uncorrelated parameters and the hybrid choice model. Specifically, we analyze three data sets focused on measuring preferences to support a renewable energy programme to grow seaweed for biogas production. In spite of the fact that all three models can converge to very similar median WTP values, they cannot be used indistinguishably. Each model is based on different assumptions, which should be tested before their use. The fact that standard sample sizes usually applied in environmental valuation are generally unable to capture the outcome differences between the models cannot be used as a justification for their indistinct application.

Sorghum as Biofuel Crop: Interdisciplinary Methods to Enhance Productivity (Botany, Genetics, Breeding, Seed Technology, and Bioengineering).

Sorghum is a versatile crop cultivated since time immemorial. It fulfills the basic needs of mankind in the contest of food, feed, fodder, nutrition, and pharmaceutical uses. Now it adds one more paramount importance as a second-generation biofuel. It offers ethanol from grain, stem (sweet sorghum), and biomass (lignocellulose), and the previous one is discouraging because of food versus fuel conflict. However sorghum lignocellulosic biofuel are gaining momentum in order to conserve nature from depleting first-generation fuel. This chapter describes interdisciplinary approaches/methods involving understanding the genetics of biofuel traits, formulating suitable breeding strategies and seed enhancement techniques to achieve higher productivity in marginal lands in order to avoid food vs. fuel conflict, and finally realization of bioethanol by involving bioengineering process. Many reviews, worldwide researches, and policy papers accepted that sorghum has tremendous potential to be used as a crop of biofuel production.

Economics of Biofuel Production: A Case of Sorghum and Pearl Millet in India.

Reduction of fossil fuels at an alarming rate has attracted increasing attention to blending biofuels worldwide. India's energy demand is expected to grow at an annual rate of 4-5 times over the next couple of decades. With self-sufficiency levels in crude oil becoming a distant dream, there is growing interest to look out for alternative fuels and the biofuels are an important option for policy makers in India. In this context, this paper reviews the experiences in India in the last two decades with respect to biofuel cultivation and its impact on land use, environment, and the livelihoods of rural communities. The objective of this paper is to assess the economics of biofuel production using Sorghum and Pearl millet feedstocks in India using a Life Cycle Analysis (LCA) approach. Baseline study was conducted during the year 2013 in the Madhya Pradesh state of India covering five districts and 333 sample farmers to understand the farmers perception about the various issues related to the production of biofuels using Indian staple food crops Sorghum and Pearl millet. Empirical data from the multi-locational trials conducted during the years 2014-2015 and 2015-2016 in farmers' fields was used to conduct the LCA analysis. Sorghum and Pearl millet feedstocks which are rain-fed crops are considered for bioethanol production with different pretreatment methods. Net Energy Ratio (NER), Net Energy Balance (NEB), Net Carbon Balance (NCB), and % Carbon reduction were some of the key parameters used for analysis and the results are evaluated based on the environmental impacts through the Life Cycle Assessment at 5% blending. Findings reveal that, dilute alkali pretreatment process is most energy intensive due to consumption of alkali consumption. Whereas dilute acid pretreatment has higher conversion efficiency than the other pretreatment processes which is due to higher glucan and xylan conversion efficiencies.The study concludes that Sorghum feedstock is more energy intensive than Pearl millet feedstock due to higher water requirement and yield. Biofuels, either conventional or advanced, should not been couraged without a comprehensive outlook on the overall impact that will ultimately have on the society, environment, or on the countries' energy security. Efforts should be made toward encouragement of research and development in the field as well as in formulating a comprehensive and effective biofuel policy for India.

Biofuels and Sustainability.

Energy security and climate change have cascading effects on the world's burgeoning population in terms of food security, environment, and sustainability. Due to depletion of fossil fuels and undesirable changes of climatic conditions, increase in air and water pollution, mankind started exploring alternate and sustainable means of meeting growing energy needs. One of the options is to use renewable sources of fuel-biofuel. In this chapter the authors have reviewed and presented sustainability impact on production of biofuels. Authors further reviewed state-of-the-art gene editing technologies toward improvement of biofuel crops. The authors recommend a phased transition from first-generation biofuel, and an acceleration toward use of technology to drive adoption of second-generation biofuels. Key aspects of technology and application of resource management models will enable these crops to bridge the global energy demand before we can completely transition to a more sustainable biofuel economy.

Microbiology challenges and opportunities in the circular economy.

Our knowledge and understanding of micro-organisms have led to the development of safe food, clean water, novel foods, antibiotics, vaccines, healthier plants, animals and soils, and more, which feeds into the United Nations Sustainable Development Goals (UN SDGs). The circular economy can contribute to the UN SDGs and micro-organisms are central to circular nutrient cycles. The circular economy as described by the Ellen MacArthur foundation has two halves, i.e. technical and biological. On the technical side, non-biological resources enter manufacturing paths where resource efficiency, renewable energy and design extend the life of materials so that they are more easily reused and recycled. Biological resources exist on the other half of the circular economy. These are used to manufacture products such as bioplastics and paper. The conservation of nature's stocks, resource efficiency and recycling of materials are key facets of the biological half of the circular economy. Microbes play a critical role in both the biological and technical parts of the circular economy. Microbes are key to a functioning circular economy, where natural resources, including biological wastes, are converted by microbes into products of value and use for society, e.g. biogas, bioethanol, bioplastics, building block chemicals and compost for healthy soils. In more recent times, microbes have also been seen as part of the tool kit in the technical side of the circular economy, where microbial enzymes can degrade plastics and microbes can convert those monomers to value-added products.