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Management of natural resources is pivotal for sustained economic growth—the increasing ecological footprints causing biocapacity deficit threaten the resource conversation agenda. The study identified the potential causes and consequences of natural resource depletion in a broad cross-section of 138 countries. Ecological footprints, international migrant stocks, industrial value-added, and population growth influenced natural resource capital across countries. The results show that ecological footprints, industrial value-added, and population growth are the detrimental factors of resource capital. In contrast, continued economic growth is helpful to conserve natural resources for future generations. The rise and fall in the natural resource degradation are evident in the wake of international migrants’ stocks to support an inverted U-shaped relationship between them. The Granger causality inferences confirmed the one-way linkages, running from international migrant stocks, economic growth, and population growth to natural resource degradation. It verifies migrants-led, affluence-led, and population-led resource degradation. Ecological footprints Granger causes industrial value-added across countries. The forecasting estimates suggested that economic growth would likely to influenced greater in magnitude to resource degradation by its innovation shocks of 4.791%, followed by international migrant stocks, population growth, ecological footprints, and industrial value added by their innovation shocks of 4.709%, 1.829%, 1.247%, and 0.700%, respectively. The study concludes that international migrant stocks should manage smartly, causing more resource degradation via a channel of increasing biocapacity deficit across countries.

In recent decades, rapid urbanization has resulted in a growing urban population, transformed into regions of exceptional socio-economic value. By removing vegetation and soil, grading the land surface and saturating soil air content, urban developments are more likely to be flooded, which will be further exacerbated by an anticipated increase in the number of intense rainfall events, due to climate change. To date, data collected show that urban pluvial flood events are on the rise for both the UK and China. This paper presents a critical review of existing sustainable approaches to urban flood management, by comparing UK practice with that in China and critically assessing whether lessons can be learnt from the Sponge City initiative. The authors have identified a strategic research plan to ensure that the sponge city initiative can successfully respond to extreme climatic events and tackle pluvial flooding. Hence, this review suggests that future research should focus on (1) the development of a more localized rainfall model for the Chinese climate; (2) the role of retrofit SuDS (Sustainable Drainage Systems) in challenging water environments; (3) the development of a robust SuDS selection tool, ensuring that the most effective devices are installed, based on local factors; and (4) dissemination of current information, and increased understanding of maintenance and whole life-costing, alongside monitoring the success of sponge cities to increase the confidence of decision makers (5) the community engagement and education about sponge cities.

Poor air quality (PAQ) is a global concern, especially in urban areas, and is often seen as an important element of social sustainability given its negative impact on health and quality of life. However, little research has been undertaken in cities of the developing world to explore how residents perceive poor air quality, its main causes, what control measures should be used to address PAQ and where the main responsibility rests for implementing control measures. The research described in this paper sought to address these points, using a questionnaire-based survey (n = 262) in Nigeria’s federal capital city of Abuja (n = 137) and the state-capital city of Enugu (n = 125). The survey took place during the COVID-19 pandemic (October 2020 to March 2021), and was stratified to ensure representation across a number of demographic groups such as gender, age, education and income. The results were analysed using the Kruskal–Wallis non-parametric test and Hochberg’s post hoc test available in SPSS version 28. The study found that the ranking of perceptual indicators and the main causes of PAQ had much agreement between respondents from both cities and between demographic groups. Smoke, odour and dust particles were perceived to be the most important indicators of PAQ, while the main sources of PAQ were waste and bush burning, vehicle use and power generators. The two most preferred control measures were proper waste management and the avoidance of bush burning. However, there was a significant difference between the two cities in terms of the main organisations responsible for addressing PAQ, with respondents from Abuja citing the federal government, while those from Enugu cited the state government. Interestingly, younger people in Enugu noted that the government should take more responsibility in controlling PAQ than did the older demographic in that city, but this difference was not seen in Abuja. Overall, this study reveals that residents in these two Nigerian cities clearly recognise their exposure to PAQ and it suggests that these perceptual indicators, and views on sources and interventions should be central to designing policies to control this important issue.

Changes in climate patterns not only affect precipitation and precipitation patterns, but also cause the spatiotemporal redistribution of precipitation and runoff, affecting hydrogeneration in turn. Based on the coupling relationship between the Coupled Model Intercomparison Project 5 (CMIP5) climate change model and surface runoff in China, a database of China’s major hydropower stations was constructed in this study and the Water Evaluation and Planning model was applied to analyze the impacts of climate change on hydropower generation in China by region and basin under the Representative Concentration Pathway (RCP)4.5 and RCP8.5 scenarios. During the forecast period, national power generation compared with base year first decreased in the 2030s and then increased in the 2070s, while a risk of excessive hydropower generation was concentrated in the southwestern provinces, Yangtze River Basin, and giant hydropower stations. During the 2030s, hydropower generation may face a risk of electricity generation decrease which will limit its contribution to the Nationally Determined Contribution target.

Cities are the important spaces, responsible entities and action units for achieving low-carbon and high-quality economy. Blockchain technology provides a new solution for low-carbon development in cities. Based on low-carbon information sharing, low-carbon incentives, low-carbon regulations with blockchain technology, this paper endeavors to propose a low-carbon development model for Chinese cities. The results indicate that: (1) blockchain-based governments, enterprises, city dwellers, and third-party service agencies are important stakeholders in promoting urban low-carbon development; (2) the effectiveness of the blockchain platform needs the coordination between the production and consumption systems, the equal emphasis on systemicity and long-term effectiveness, and the deep integration of government regulations and market mechanism; (3) blockchain-based information sharing instruments, incentive instruments, and regulation instruments should be applied to low-carbon development in cities; (4) blockchain technology promotes low-carbon development for cities in both direct ways and indirect ways. This study can provide a reference for the urban sustainable development in China and other countries.

Today, introducing useful and practical solutions to residential load disaggregation as subsets of energy management has created numerous challenges. In this study, an intelligence hybrid solution based on manifold learning and deep learning applications is presented. The proposed solution presents a combined structure of Laplacian eigenmaps (LE), a convolutional neural network (CNN), and a recurrent neural network (RNN), called LE-CRNN. In the proposed model architecture, LE, with its high ability in dimensional reduction, transfers the salient features and specific values of power consumption curves (PCCs) of household electrical appliances (HEAs) to a low-dimensional space. Then, the combined model of CRNN significantly improves the structure of CNN in fully connected layers so that the process of identification and separation of the HEA type can be performed without overfitting problems and with very high accuracy. In order to implement the suggested model, two real-world databases have been used. In a separate scenario, a conventional CNN is applied to the data for comparing the performance of the suggested model with the CNN. The designed networks are trained and validated using the PCCs of HEAs. Then, the whole energy consumption of the building obtained from the smart meter is used for load disaggregation. The trained networks, which contain features extracted from PCCs of HEAs, prove that they can disaggregate the total power consumption for houses intended for the Reference Energy Disaggregation Data Set (REDD) and Almanac of Minutely Power Dataset (AMPds) with average accuracies (Acc) of 97.59% and 97.03%, respectively. Finally, in order to show the accuracy of the developed hybrid model, the obtained results in this study are compared with the results of similar works for the same datasets.

In order to fulfil the UK government’s ambitious goal of 80% reductions in greenhouse gas emissions by 2050 compared to the levels of 1990s, unprecedented measures for improving the energy efficiency of buildings are needed. This study investigates the impact of a specific type of Low-emissivity (Low-E) window film—Thinsulate Climate Control 75—on the holistic energy consumption of an existing United Kingdom (UK) hotel building. Building modelling and energy simulation software EDSL TAS is used to conduct the study. The result of the simulations demonstrates that by applying Thinsulate films, savings in heating, cooling, and total energy consumptions are achieved by 3%, 20%, and 2.7%, respectively. Also 4.1% and 5.1% savings are achieved in annual CO2 emissions and total energy costs, respectively, while the initial costs may be an issue. This study found that application of Low-E window films results in slightly better energy performance of the hotel regarding its heating-dominant climate. The study also recommends using average annual actual energy consumption data for a time range, instead of picking a single year’s data for validating purposes.