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Bioplastics are alternatives of conventional petroleum-based plastics. Bioplastics are polymers processed from renewable sources and are biodegradable. This study aims at conducting an environmental impact assessment of the bioprocessing of agricultural wastes into bioplastics compared to petro-plastics using an LCA approach. Bioplastics were produced from potato peels in laboratory. In a biochemical reaction under heating, starch was extracted from peels and glycerin, vinegar and water were added with a range of different ratios, which resulted in producing different samples of bio-based plastics. Nevertheless, the environmental impact of the bioplastics production process was evaluated and compared to petro-plastics. A life cycle analysis of bioplastics produced in laboratory and petro-plastics was conducted. The results are presented in the form of global warming potential, and other environmental impacts including acidification potential, eutrophication potential, freshwater ecotoxicity potential, human toxicity potential, and ozone layer depletion of producing bioplastics are compared to petro-plastics. The results show that the greenhouse gases (GHG) emissions, through the different experiments to produce bioplastics, range between 0.354 and 0.623 kg CO2 eq. per kg bioplastic compared to 2.37 kg CO2 eq. per kg polypropylene as a petro-plastic. The results also showed that there are no significant potential effects for the bioplastics produced from potato peels on different environmental impacts in comparison with poly-β-hydroxybutyric acid and polypropylene. Thus, the bioplastics produced from agricultural wastes can be manufactured in industrial scale to reduce the dependence on petroleum-based plastics. This in turn will mitigate GHG emissions and reduce the negative environmental impacts on climate change.

Biogas is a viable alternative for supplying clean and sustainable energy. Despite all manner of policy measures introduced by the Government of India, biogas is not widely used in India. This article tries to identify factors that influence the decision to adopt biogas at household level. We examine a conceptual framework empirically in which a household wants to maximize utility from biogas by using the India Human Development Survey (IHDS) I, which is a nationally representative, multi-topic survey. By applying both maximum likelihood and penalized likelihood methods (Firthlogit) of logistic regression on a sample size of almost 10,384 households, it has been found that wealthy people are more likely to adopt biogas compared to the marginalized section of the society. We recommend more inclusive policy measures for the weaker section of the society to create an enabling environment to make it a self-promoting technology.

Abstract Globally, three-quarter of a billion people live without electricity. Besides, hundreds of million use a solar lantern for less than 4 h a day. Most of the access-deprived are in the Global South, predominantly in Sub-Saharan Africa and South Asia. The United Nations framed the seventh Sustainable Development Goal to improve access. Solar photovoltaic powered mini-grids are increasingly extending better service to deprived regions. However, poor load-factor and expensive storage adversely affect viability. Also, these mini-grids do not support infrequent large loads to avoid further loss of load-factor. Electric cooking is efficient and non-polluting; water treatment facilities can save millions from contaminant and pathogen by providing clean water. Besides, both electric cooking and water treatment are less expensive than alternatives. But mini-grids frequently do not support these. Indeed, the presence of sustained productive loads favourably influences the mini-grid economy. This study investigates the role of critical household loads to deliver similar bearing on the mini-grid economy. Results underscore realisation of desirable impact with household collaboration under a demand-response program. Collaborative consumption can lower initial investment by 62% and reduce the unit energy cost to $0.23. Also, cooperation improves the mini-grid load factor and promotes viability. Additionally, fast deployment needs during and after Covid-19 remains inherently supported while mitigating the pandemic induced financial stress of both consumer and mini-grid operator. This study of 88 nation-states underscores that demand response in a mini-grid can not only improve affordability for all consumers, but it can also bring 186 million people within affordable access.

Abstract A variety of novel, recyclable and reusable, construction materials has already been studied within literature during the past years, aiming at improving the overall energy efficiency ranking of the building envelope. However, several studies show that a delicate control of indoor climating elements can lead to a significant performance improvement by exploiting the building’s savings potential via smart adaptive HVAC regulation to exogenous uncertain disturbances (e.g. weather, occupancy). Building Optimization and Control (BOC) systems can be categorized into two different groups: centralized (requiring high data transmission rates at a central node from every corner of the overall system) and decentralized 1 (assuming an intercommunication among neighboring constituent systems). Moreover, both approaches can be further divided into two subcategories, respectively: model-assisted (usually introducing modeling oversimplifications) and model-free (typically presenting poor stability and very slow convergence rates). This paper presents the application of a novel, decentralized, agent-based , model-free BOC methodology (abbreviated as L4GPCAO) to a modern non-residential building (E.ON. Energy Research Center’s main building), equipped with controllable HVAC systems and renewable energy sources by utilizing the existing Building Management System (BES). The building testbed is located inside the RWTH Aachen University campus in Aachen, Germany. A combined rule criterion composed of the non-renewable energy consumption (NREC) and the thermal comfort index – aligned to international comfort standards – was adopted in all cases presented herein. Besides the limited availability of the specified building testbed, real-life experiments demonstrated operational effectiveness of the proposed approach in BOC applications with complex, emerging dynamics arising from the building’s occupancy and thermal characteristics. L4GPCAO outperformed the control strategy that was designed by the planers and system provider, in a conventional manner, requiring no more than five test days.

Abstract Natural resources exist independent of human intervention. Although these interventions can and do affect the balance between ecological and biological diversity conditions these resources support, and their use to promote economic development. Currently, the unsustainable use of these resources threatens this balance, calling for more sustainable patterns of natural resource use and conservation. The primary responsibility for ensuring the proper balance lies with governments, leading to various policies and programs to preserve natural resources. The ultimate goal is to make the masses aware of natural assets’ importance and encourage their sustainable use. To successfully implement, however, these government practices require public communication and participation, and the full consideration of public opinion at various levels of governance. A predictive analytics framework is proposed for understanding public opinion on government policies to improve sustainable water governance. An integrated policy initiative to balance water resources use and conservation launched by the Indian government served as a test case for applying the framework in an attempt to accurately classify the opinion polarity related to the policy. The conventional feature extraction is applied to pre-processed datasets to extract the relevant features. Subsequently, swarm-based feature selection is applied to filter out optimal features. Lastly, opinion mining and textual analysis are performed to determine the most relevant water management factors that need immediate attention. The proposed framework serves as a policy evaluation strategy in the water management domain. The paper closes with a discussion of the general applicability of the proposed framework.

SummaryThe German government has adopted a law that requires sewage plants to go beyond the recovery of phosphorus from wastewater and to promote recycling. We argue that there is no physical global short‐ or mid‐term phosphorus scarcity. However, we also argue that there are legitimate reasons for policies such as those of Germany, including: precaution as a way to ensure future generations’ long‐term supply security, promotion of technologies for closed‐loop economics in a promising stage of technology development, and decrease in the current supply risk with a new resource pool.

Abstract Since the adoption of the Kyoto protocol in 1997 and its entry into force in 2005, as well its aftermath such as the Doha amendment and Paris agreement, national policies have become more conscious of the usage of clean energy, mostly the different forms of renewable energy and nuclear energy. Ratifying countries and signatories had committed themselves to binding targets for the reduction of greenhouse emissions by 8% with respect to 1990 levels until 2012, also based on the particular contribution to global emissions from each country. This paper examines the integrational properties of clean energy consumption from eight emerging economies which are also high greenhouse gas emitters. The empirical results show that the clean energy consumption is stationarity for Brazil and Philippines by using a quantile unit root test without smooth breaks (Koenker and Xiao, 2004). However, after capturing the smooth breaks (Bahmani-Oskooee et al., 2018), we find the clean energy consumption of China, Pakistan and Thailand are stationary. The time-varying deterministic trend with smooth breaks is more fitted to the path of clean energy consumption in comparison to the deterministic trend without smooth breaks. The paper suggests economic insights useful for policy making.

Abstract This paper provides a forecast of electricity consumption in Cyprus up to the year 2030, based on econometric analysis of energy use as a function of macroeconomic variables, prices and weather conditions. If past trends continue electricity use is expected to triple in the coming 20–25 years, with the residential and commercial sectors increasing their already high shares in total consumption. Besides this reference scenario it was attempted to assess the impact of climate change on electricity use. According to official projections, the average temperature in the Eastern Mediterranean is expected to rise by about 1 °C by the year 2030. Using our econometrically estimated model, we calculated that electricity consumption in Cyprus may be about 2.9% higher in 2030 than in the reference scenario. This might lead to a welfare loss of 15 million Euros in 2020 and 45 million Euros in 2030; for the entire period 2008–2030 the present value of costs may exceed 200 million Euros (all expressed in constant Euros of 2007). Moreover, we assessed the additional peak electricity load requirements in the future because of climate change: extra load may amount to 65–75 Megawatts (MW) in the year 2020 and 85–95 MW in 2030.

Residential buildings constitute roughly one-fourth of the total energy use across the globe. Numerous studies have shown that providing an energy breakdown increases residents' awareness of energy use and can help save up to 15% energy. A significant amount of prior work has looked into source-separation techniques collectively called non-intrusive load monitoring (NILM), and most prior NILM research has leveraged high-frequency household aggregate data for energy breakdown. However, in practice most smart meters only sample hourly or once every 15 minutes, and existing NILM techniques show poor performance at such a low sampling rate. In this paper, we propose a TreeCNN model for energy breakdown on low frequency data. There are three key insights behind the design of our model: i) households consume energy with regular temporal patterns, which can be well captured by filters learned in CNNs; ii) tree structure isolates the pattern learning of each appliance that helps avoid magnitude variance problem, while preserves relationship among appliances; iii) tree structure enables the separation of known appliance from unknown ones, which de-noises the input time series for better appliance-level reconstruction. Our TreeCNN model outperformed seven existing baselines on a public benchmark dataset with lower estimation error and higher accuracy on detecting the active states of appliances.