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Global fossil fuel reserves are declining due to differential uses, especially for power generation. Everybody can help to do their bit for the environment by using solar energy. Geographically, Bangladesh is a potential zone for harnessing solar energy. In March 2021, the renewable generation capacity in Bangladesh amounted to 722.592 MW, including 67.6% from solar, 31.84% from hydro, and 0.55% from other energy sources, including wind, biogas, and biomass, where 488.662 MW of power originated from over 6 million installed solar power systems. Concurrently, over 42% of rural people still suffer from a lack of electricity, where solar energy can play a vital role. This paper highlights the present status of various forms of solar energy progress in Bangladesh, such as solar parks, solar rooftops, solar irrigation, solar charging stations, solar home systems, solar-powered telecoms, solar street lights, and solar drinking water, which can be viable alternative sources of energy. This review will help decision-makers and investors realize Bangladesh’s up-to-date solar energy scenario and plan better for the development of a sustainable society.

In recent years, prices for photovoltaics have fallen steadily and the demand for sustainable energy has increased. Consequentially, the assessment of roof surfaces in terms of their suitability for PV (Photovoltaic) installations has continuously gained in importance. Several types of assessment approaches have been established, ranging from sampling to complete census or aerial image analysis methodologies. Assessments of rooftop photovoltaic potential are multi-stage processes. The sub-task of examining the photovoltaic potential of individual rooftops is crucial for exact case study results. However, this step is often time-consuming and requires lots of computational effort especially when some form of intelligent classification algorithm needs to be trained. This often leads to the use of sampled rooftop utilization factors when investigating large-scale areas of interest, as data-driven approaches usually are not well-scalable. In this paper, a novel neighbourhood-based filtering approach is introduced that can analyse large amounts of irradiation data in a vectorised manner. It is tested in an application to the city of Giessen, Germany, and its surrounding area. The results show that it outperforms state-of-the-art image filtering techniques. The algorithm is able to process high-resolution data covering 1 km2 within roughly 2.5 s. It successfully classifies rooftop segments which are feasible for PV installations while omitting small, obstructed or insufficiently exposed segments. Apart from minor shortcomings, the approach presented in this work is capable of generating per-rooftop PV potential assessments at low computational cost and is well scalable to large scale areas.

The united nations educational, scientific and cultural organization (UNESCO) considers the historic urban landscapes as the world heritages. Managing historic city centers and maintaining historic cores are the emerging challenges for sustainable urban planning. Today, the historic cores form an important part of the economic, social, environmental, and physical assets and capacities of contemporary cities, and play a strategic role in their development. One of the most important approaches to the development of central textures, especially in historical and cultural cities, is the sustainable urban regeneration approach, which encompasses all aspects of sustainability, such as the economic, social, cultural and environmental aspects. To maintain sustainability and regeneration of historic cores of cities, it is necessary to provide insight into the underlying characteristics of the local urbanization. Furthermore, the fundamental assets are to be investigated as indicators of sustainable regeneration and drivers of urban development. In the meantime, a variety of research and experience has taken place around the world, all of which has provided different criteria and indicators for the development of strategies for the historic cores of cities. The present study, through a meta-analytic and survey method, analyzing the experience and research reported in 139 theoretical and empirical papers in the last twenty years, seeks to provide a comprehensive conceptual model taking into account the criteria and indices of sustainable regeneration in historic cores of cities. The quality of the survey has been ensured using the preferred reporting items for systematic reviews and meta-analysis (PRISMA).

Under the “new normal”, China is facing more severe carbon emissions reduction targets. This paper estimates the carbon emission data of various provinces in China from 2008 to 2014, constructs a revised gravity model, and analyzes the network structure and effects of carbon emissions in various provinces by using social network analysis (SNA) and quadratic assignment procedure (QAP) analysis methods. The conclusions show that there are obvious spatial correlations between China’s provinces and regions in terms of carbon emissions: Tianjin, Shanghai, Zhejiang, Jiangsu and Guangdong are in the center of the carbon emission network, and play the role of “bridges”. Carbon emissions can be divided into four blocks: “bidirectional spillover block”, “net beneficial block”, “net spillover block” and “broker block”. The differences in the energy consumption, economic level and geographical location of the provinces have a significant impact on the spatial correlation relationship of carbon emissions. Finally, the improvement of the robustness of the overall network structure and the promotion of individual network centrality can significantly reduce the intensity of carbon emissions.

We report on the real-world use over the course of one year of a nickel-metal-hydride plug-in hybrid—the Toyota Plug-In HV—by a set of 12 northern California households able to charge at home and work. From vehicle use data, energy and greenhouse-emissions implications are also explored. A total of 1557 trips—most using under 0.5 gallons of gasoline—ranged up to 2.4 hours and 133 miles and averaged 14 minutes and 7 miles. 399 charging events averaged 2.6 hours. The maximum lasted 4.6 hours. Most recharges added less than 1.4 kWh, with a mean charge of 0.92 kWh. The average power drawn was under one-half kilowatt. The greenhouse gas emissions from driving and charging were estimated to be 2.6 metric tons, about half of the emissions expected from a 22.4-mpg vehicle (the MY2009 fleet-wide real-world average). The findings contribute to better understanding of how plug-in hybrids might be used, their potential impact, and how potential benefits and requirements vary for different plug-in-vehicle designs. For example, based on daily driving distances, 20 miles of charge-depleting range would have been fully utilized on 81% of days driven, whereas 40 miles would not have been fully utilized on over half of travel days.

This study aims to overview the U.S. sustainable development by measuring the environmental performance of 50 states over the period of 2009–2018. To attain the objective, we employ data envelopment analysis for environmental assessment where we prioritize the minimization of CO2 emissions first and the maximization of gross state product later under the concept of managerial disposability (i.e., an environment-based performance measure). Then, we examine how the state-level environmental performance measures are associated with their political and spatial contexts. For the purpose, we conduct the Kruskal-Wallis rank sum test across groups of states characterized by their political transitions in the presidential and gubernatorial elections and defined by the regions of the U.S. Economic Development Administration and Environmental Protection Agency. Based on our empirical results, we find that (a) overall environmental performance has gradually enhanced over time, (b) there are statistically significant differences in the environmental performance measures along with the political transitions, and (c) states on both coasts have outperformed those of the middle in the measurement.

Carbon capture, utilization, and storage (CCUS) is an attractive technology for the decarbonization of global energy systems. However, its early development stage makes impact assessment difficult. Moreover, rising popularity in carbon pricing necessitates the development of a methodology for deriving carbon abatement costs that are harmonized with the price of carbon. We develop, using a combined bottom-up analysis and top-down learning curve approach, a levelized cost of carbon abatement (LCCA) model for assessing the true cost of emissions mitigation in CCUS technology under carbon pricing mechanisms. We demonstrate our methodology by adapting three policy scenarios in Canada to explore how the implementation of CO2-to-diesel technologies could economically decarbonize Canada’s transportation sector. With continued policy development, Canada can avoid 932 MtCO2eq by 2075 at an LCCA of CA$209/tCO2eq. Technological learning, low emission hydroelectricity generation, and cost-effective electricity prices make Quebec and Manitoba uniquely positioned to support CO2-to-diesel technology. The additional policy supports beyond 2030, including an escalating carbon price, CO2-derived fuel blending requirements, or investment in low-cost renewable electricity, which can accelerate market diffusion of CO2-to-diesel technology in Canada. This methodology is applicable to different jurisdictions and disruptive technologies, providing ample foci for future work to leverage this combined technology learning + LCCA approach.