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Purpose This study aims to consider environmental sustainability, a global challenge under the preview of sustainable development goals, highlighting the significance of knowledge economy in attaining sustainable aggregate demand behavior globally. For this purpose, 155 countries that have data available from 1995 to 2021 were selected. The purpose of selecting these countries is to test the global responsibility of the knowledge economy to attain environmental sustainability. Design/methodology/approach Results are estimated with the help of panel quantile regression. The empirical existence of aggregate demand-based environmental Kuznets curve (EKC) was tested using non-linear tests. Moreover, principal component analysis has been incorporated to construct the knowledge economy index. Findings U-shaped aggregate demand-based EKC at global level is validated. However, environmental deterioration increases with an additional escalation after US$497.945m in aggregate demand. As a determinant, the knowledge economy is reducing CO2 emissions. The knowledge economy has played a significant role in global responsibility, shifting the EKC downward and extending the CO2 reduction phase for every selected country. Further, urbanization, energy intensity, financial development and trade openness significantly deteriorate the environmental quality. Originality/value This study contains the empirical existence of aggregate demand-based EKC. The role of the knowledge economy is examined through an index which is calculated by using four pillars of the knowledge economy (technology, innovations, education and institutions). This study is based on a combined panel of all the countries for which the data was available.

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 In the context of climate change, global industrialised nations are grappling with transforming energy networks to support a low carbon future. Using an energy justice framework this work aims to understand holistic outcomes of one low-carbon energy network intervention: demand-side response enacted on domestic heat pumps. By exploring participants’ lived experience of a pilot project, from recruitment to installation and use, this work reveals how injustices were reduced, introduced and amplified. Choice, consent, cost, comfort, disruption, and control are highlighted as key aspects of interest when considering the distributive, procedural, and recognition implications of this domestic innovation. For a net reduction of energy injustices to be realised, we highlight the need for project designers to work in partnership with end users to optimise the benefits for the household and the electricity system. Whilst this is a UK study, the themes and findings are internationally applicable for interventions that aim to harness the flexibility of heating, the largest global energy end-use.

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 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.

Life Cycle Assessment (LCA) has matured over the past decades and become part of the broader field of sustainability assessment. To strengthen LCA as a tool and eventually increase its usefulness for sustainability decision-making, it is argued that there is a need to expand the ISO LCA framework by integration and connection with other concepts and methods. This paper explores the potential options for deepening and broadening the LCA methodologies beyond the current ISO framework for improved sustainability analysis. By investigating several environmental, economic and social assessment methods, the paper suggests some options for incorporating (parts of) other methods or combining with other methods for broadening and deepening the LCA.

The deposition of ash - combustion residues - on superheaters and heat exchanger surfaces reduce their efficiency; this phenomenon was investigated for a large-scale waste-to-energy incineration facility. Over a period of six months, ash samples were collected from the plant, which included the bottom ash and deposits from the superheater, as well as flyash from the convective heat exchanger, the economiser and fabric filters. These were analysed for particle size, unburned carbon, elemental composition and surface morphology. Element partitioning was evident in the different combustion residues, as volatile metals, such as cadmium, antimony and arsenic, were found to be depleted in the bottom ash by the high combustion temperatures (1000+°C) and concentrated/enriched in the fabric filter ash (transferred by evaporation). Non-volatile elements by contrast were distributed equally in all locations (transported by particle entrainment). The heat exchanger deposits and fabric filter ash had elevated levels of alkali metals. 82% of flyash particles from the fabric filter were in the submicron range.

One of the major environmental problems is the fast accumulation of brick kiln emissions near the peripheries of almost all urban centers. An enormous quantity of substandard coal, wood, rubber tires, or saw wood is used as fuel in these brick kilns, creating environmental pollution through carbon and heavy metals present in the emissions. The present study was designed to determine the distribution pattern of heavy metals in a soil-water-plant system under the influence of brick kiln emissions. Ash, soil (0-15 cm), water, and plant samples were collected from the adjacent areas of the brick kilns. Geo-accumulation index and contamination factors were used to assess the toxicity of metals to soil and plants. It was found that the concentrations of Cd in water, soil, coal, and plant samples were found in the range of 0.004-0.024 mg L-1, 0.02-0.66, 0.002-0.042, and 0.06 to 0.07 mg kg-1, respectively, while Pb concentration was found within the range of 0-0.08 mg L-1, 0.30-8.60, 0.011-0, and 0.01-0.08 mg kg-1 in water, soil, and plant samples, respectively. The concentration of Cd in brick kilns ash was in the range of 0-0.04 mg kg-1 while Pb was found within the range of 0.01-0.05 mg kg-1 for ash. Geo-accumulation index and contamination factor values for Cd were found higher suggesting proper pollution prevention measures to be implemented. Based on the results, it was concluded that Cd concentrations in soil, plants, and water samples were higher in the subject area due to brick kiln emissions and higher Cd concentrations in the fuel material.

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.