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Abstract Integration of solar photovoltaic (PV) and battery storage systems is an upward trend for residential sector to achieve major targets like minimizing the electricity bill, grid dependency, emission and so forth. In recent years, there has been a rapid deployment of PV and battery installation in residential sector. In this regard, optimal planning of PV-battery systems is a critical issue for the designers, consumers, and network operators due to high number of parameters that can affect the optimization problem. This paper aims to present a comprehensive and critical review on the effective parameters in optimal planning process of solar PV and battery storage system for grid-connected residential sector. The key parameters in process of optimal planning for PV-battery system are recognized and explained. These parameters are economic and technical data, objective functions, energy management systems, design constraints, optimization algorithms, and electricity pricing programs. A timely review on the state-of-the-art studies in PV-battery optimal planning is presented. The challenges, trends and latest developments in the topic are discussed. At the end, scopes for future studies are developed. It is found that new guidelines should be provided for the customers based on various electricity rates and demand response programs. Also, several design considerations like grid dependency and resiliency need further investigation in the optimal planning of PV-battery systems.

Abstract Kenya has made considerable policy efforts to expand its renewable energy portfolio to meet energy demand and mitigate greenhouse gas (GHG) emissions. Development of proper policies requires a robust framework for analyzing the benefits of renewable energy investments. Towards this end, this study applied a choice experiment analysis to determine how attributes (type of energy, ownership, impact on environment, distance and visibility, community job creation, and yearly renewable energy tax) impact the public willingness to pay for renewable energy development in Kenya. A nationwide survey of 1020 households was conducted in nine counties using conditional logit (MNL) and random parameter logit (RPL) frameworks. The results reveal that the Kenyan public places a high value on environmental impact, followed by type of renewable energy and community job creation, respectively. On the other hand, respondents do not place much emphasis on ownership or distance and visibility. Policy simulation suggests that while renewable energy adoption is highly valued by households, the total willingness to pay is not enough to cover the higher capital cost for the development of various renewable energy technologies.

Integrated assessment models (IAMs) are playing an increasingly important role in long-run sustainability analysis. At their core is a set of global economic and environmental accounts which capture a complete set of inter-industry and inter-regional relationships in the global economy in a consistent manner. While much attention is focused on the raw data and parameterization required to expand or add sectoral detail to IAMs, only rarely is there discussion of how different matrix balancing methods (i.e. translating disparate raw data sources into the consistent database) affect modeling results. This article offers an in-depth look into the database–modeling nexus in IAMs, focusing on the electric power sector which is both a major source of CO2 emissions and a critical vehicle for climate change mitigation. Comparisons of the prevailing matrix balancing algorithms show how the choice of database reconciliation methodology affects modeling results using policy-relevant simulations in the context of the electric power sector. The resulting insights can be applied to the disaggregation of other, technology rich sectors in the economy. We conclude that appropriate selection of database reconciliation methodologies can reduce the deviation between bottom-up and top-down modeling.

Abstract Real-world vehicle operating mode data (2.5 million 1 Hz records), collected by instrumenting the vehicles of 82 volunteer drivers with OBD datalogger and GPS while they drove their routine travel routes, were analyzed to quantify vehicle emissions estimate errors due to road grade and driving style in rural, hilly Vermont. Data were collected in winter and summer for MY 1996 and newer passenger cars and trucks only. EPA MOVES2010b was used to estimate running exhaust emissions associated with measured vehicle activity. Changes in vehicle specific power (VSP) and MOVES operating mode (OpMode) due to proper accounting for real-world road grade indicated emission rate errors between 10% and 48%, depending on pollutant, chiefly because grade-related changes in VSP could shift activity by as many as six OpModes, depending on road type. The correct MOVES OpMode assignment was made only 33–55% of the time when road grade was not included in the VSP calculation. Driving style of individual drivers was difficult to assess due to unknown traffic operations data, but the largest differences between individual drivers were observed on rural restricted roads, where traffic conditions and control have minimal impact. The results suggest the importance of (1) measuring and incorporating real-world road grade in order to correctly assign MOVES emission rates; and (2) developing a driving style typology to account for differences in the MOVES emissions estimates due to driver variability.

Co-generation of energy derived from human movement is not new. Intentionally accumulating energy, from mass urban-mobility, provides opportunities to re-purpose power. However, when mass-mobility is predictable, yet not harnessed, this highlights critical gaps in application of interdisciplinary knowledge. This research highlights a novel application of geostatistical modelling for the built environment with the purpose of understanding where energy harvesting infrastructure should be located. The work presented argues that advanced Geostatistical methods can be implemented as an appropriate method to predict probability distribution, density, clustering of populations and mass-population mobility patterns from large-scale online distributed and heterogeneous data sets published by the Australian Urban Research Infrastructure Network. Where clear urban spatio-behavioural relationships of density and movement can be predicted – understanding such patterns supports cross-disciplinary city planning and decision-making. A data-informed – predictive spatial decision-making framework is proposed – facilitating the endeavour of cogenerating kinetic human energy within a prescribed space. This novel proposition could further sustainability strategies for compact living for cities such as in Perth, Western Australia which is increasingly economically and geographically pressured to densify. This research argues that surveillance data elucidate a capacity to interpret and understand impacts of densification strategies, efficacy of CCTV networks in existing and emerging cities.

Based on an extensive literature review and research interviews of energy experts, this article asks: what are the remaining impediments to clean energy systems and how can a Post-Kyoto Protocol climate framework be designed to overcome them? The article begins by exploring commercially available 'clean' energy systems and practices relating to energy end-use and infrastructure, energy supply, carbon capture and storage, and non-CO2 related greenhouse gas emissions. The article then examines a selection of persistent financial, market, information and intellectual property barriers. Lastly, it articulates the implication of these barriers for the design of future national and international climate change policies.

In an era of environmental crisis, the world is at a perilous moment. The fight between sustainability and economical choice has been a contentious matter and the world is facing environmental uncertainties today. The main purpose of the present study is to prepare an integrated exploration of the development of consciousness in all business categories. This study also focuses on the indispensable roles and impact of top management in merging the commitment and abilities to environmental leadership and thus, adds a contemporary insight into how businesses, economic stakeholders, government authorities, and the general human sphere can mitigate the runaway menace of environmental issues. Finally, the current authors conclude that the environment is the supernatural force that is making life a possibility on the planet. Therefore, every human operation should be regulated by the approaches mentioned in this study and efforts made through the strategic principles and theories of leadership.

In recent years, sustainability concerns have gained increasing attention among countries and stakeholders worldwide. Towards the transition to sustainable rural development, the rural web framework (RWF) has become a consistent tool. Indicators from the RWF have been used to explore sustainable rural development for decision-making tasks, which improves the social, economic, and environmental performance of rural regions. However, the application of the RWF in studies is on the decline. Furthermore, there is a lack of literature reviews on the importance of the RWF and its relationship with different facets of sustainable development. We conducted a systematic literature review (SLR) (a) to explore how studies have used the RWF in the context of sustainable development and (b) to identify areas for further research. This study found that the RWF has mostly been used in developed countries, with fewer applications in developing countries. We suggest that there should be increased application of the RWF, particularly in developing countries, to broaden the rural web–sustainable development discourse and its relevance. This paper presents several areas where the indicators of the RWF can be applied to illustrate their relevance for policy decisions towards the achievement of the sustainable development goals (SDGs).

This study examined the effects that sustainable fashion education had on college students��� attitudes, subjective norms, knowledge, and intention to make sustainable apparel choices. In addition, relationships were analyzed and interpreted between intention and attitudes, subjective norms, and knowledge. The need for this research stems from changes in the fashion industry that required the adoption of new business models. The circular economy model embraces a culture that makes, consumes, enriches, or returns the product to supply chains. For the circular model to be successful, all stakeholders must understand the role one plays in creating a sustainable industry. The consumer is an essential player in the circular model. Overconsumption and underutilization of clothing by the consumer are currently not sustainable. This quantitative study was guided by Ajzen���s theory of planned behavior that predicts individual intention to engage in a behavior in which the person can exert self-control. This study followed a quasi-experimental design with paired t test and correlational analysis, collecting from a sample of 97 college students. Pretest and posttest survey data was gathered from college students before and after they completed a series of online learning modules about fast fashion and sustainable fashion. This study provided evidence that educational intervention influences a significant change in subjective norms, attitudes, knowledge, and intention. Research findings show that subjective norms and attitudes had significant relationships with intention to make sustainable apparel choices. The findings from this research support a need for education about the impacts apparel and clothing choices have on our environment. Results also provide evidence that education makes a positive impact. Conclusions from the research provide implications for FCS professionals exploring topics and approaches for educating others about sustainable apparel production, consumption and care, and a return to production.