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Localization of the nodes is crucial for gaining access of different nodes which would provision in extreme areas where networks are unreachable. The feature of localization of nodes has become a significant study where multiple features on distance model are implicated on predictive and heuristic model for each set of localization parameters that govern the design on energy minimization with proposed adaptive threshold gradient feature (ATGF) model. A received signal strength indicator (RSSI) model with node estimated features is implicated with localization problem and enhanced with hybrid cumulative approach (HCA) algorithm for node optimizations with distance predicting.

Using a theoretical or empirical signal propagation model, the RSSI (known transmitting power) is converted to distance, the received power (measured at the receiving node) is converted to distance and the distance is converted to RSSI (known receiving power). As a result, the approximate distance between the transceiver node and the receiver may be determined by measuring the intensity of the received signal. After acquiring information on the distance between the anchor node and the unknown node, the location of the unknown node may be determined using either the trilateral technique or the maximum probability estimate approach, depending on the circumstances using federated learning.

Improvisation of localization for wireless sensor network has become one of the prime design features for estimating the different conditional changes externally and internally. One such feature of improvement is observed in this paper, via HCA where each feature of localization is depicted with machine learning algorithms imparting the energy reduction problem for each newer localized nodes in Section 5. All affected parametric features on energy levels and localization problem for newer and extinct nodes are implicated with hybrid cumulative approach as in Section 4. The proposed algorithm (HCA with AGTF) has implicated with significant change in energy levels of nodes which are generated newly and which are non-active for a stipulated time which are mentioned and tabulated in figures and tables in Section 6.

Localization of the nodes is crucial for gaining access of different nodes which would provision in extreme areas where networks are unreachable. The feature of localization of nodes has become a significant study where multiple features on distance model are implicated on predictive and heuristic model for each set of localization parameters that govern the design on energy minimization with proposed ATGF model. An RSSI model with node estimated features is implicated with localization problem and enhanced with HCA algorithm for node optimizations with distance predicting.

Energy poverty is a global phenomenon, but its prevalence is enormous in most African countries, with a potential impact on quality of life. This study aims to investigate the impact of energy poverty on the shadow economy.

The study uses panel data from 45 countries in Africa over a period of 1996–2018. Using panel cointegrating regression and panel vector auto-regression model in the generalized method of moments technique.

This study provides that energy poverty deepens the size of the shadow economy in Africa. It also documents that there is a bidirectional causality between shadow economy and energy poverty. Therefore, the two variables can predict each other.

The study suggests that lack of access to clean and modern energy services contributes to the depth of the shadow economy in Africa. African authorities are advised to strengthen rural and urban electrification initiatives by providing adequate energy infrastructure so as to reduce the level of energy poverty in the region. To ensure energy sustainability delivery, the study proposes that the creation of national and local capacities would be the most effective manner to guarantee energy accessibility and affordability. Also, priorities should be given to the local capital mobilization and energy subsidies for the energy poor. Energy literacy may also contribute to the sustainability and the usage of modern energy sources in Africa.

Previous studies reveal that income inequality contributes to the large size of shadow economy in developing economies. However, none of these studies analyzed the role of energy poverty and its implications for underground economic operations. Inadequate access to modern energy sources is likely to deepen the prevalence of informality in developing nations. Based on this, this study provides fresh evidence on the implications of energy deprivation on the shadow economy in Africa using a heterogeneous panel econometric framework. The study contributes to the literature by advocating that the provision of affordable modern energy sources for rural and urban settlements, and the creation of good energy infrastructure for the firms in the formal economy would not only improve the quality of life but also important to discourage underground economic operations in developing economies.

The current literature has not investigated the perceived value of energy efficiency by households, regardless of financial benefits. Furthermore, there is a severe lack of research that investigates the effectiveness of the current format of EPC-labels. Therefore, the purpose of this paper is twofold: to study how households value energy efficiency in the housing market, regardless of price effects.

This study uses multiple hedonic regression models to analyse 706,778 Flemish properties for sale or rent between 2019 and 2023. The data is provided by Immoweb – the largest online real estate platform in Belgium. Given that the selling market is driven by different mechanisms than the rental market, the data set was divided in sold (522,164 listings) and rented properties (184,614 listings).

The ambiguous results of the A-label in the selling market indicate that the “class evaluation effect” found in related markets which use labels (e.g. household appliances) is also present in the housing market. However, the results of the other (lower) labels clearly show that owners do value energy improvements within labels, and this effect becomes stronger as the EPC-label becomes better. The rental market shows the opposite results. Energy improvements are only valued if they translate into a financial benefit. Taking these findings into account, the second part of this research shows that rescaling the EPC-label creates an incentive for improvements within labels.

This paper provides novel insights by studying the perceived value of energy efficiency in the absence of financial benefits and critically studying the effectiveness of the EPC-labels in their current shape. By investigating both the sales and rental market, the authors are able to make a comparison which creates valuable insights for academia, governments and real estate professionals.

Predictive digital twin technology, which amalgamates digital twins (DT), the internet of Things (IoT) and artificial intelligence (AI) for data collection, simulation and predictive purposes, has demonstrated its effectiveness across a wide array of industries. Nonetheless, there is a conspicuous lack of comprehensive research in the built environment domain. This study endeavours to fill this void by exploring and analysing the capabilities of individual technologies to better understand and develop successful integration use cases.

This study uses a mixed literature review approach, which involves using bibliometric techniques as well as thematic and critical assessments of 137 relevant academic papers. Three separate lists were created using the Scopus database, covering AI and IoT, as well as DT, since AI and IoT are crucial in creating predictive DT. Clear criteria were applied to create the three lists, including limiting the results to only Q1 journals and English publications from 2019 to 2023, in order to include the most recent and highest quality publications. The collected data for the three technologies was analysed using the bibliometric package in R Studio.

Findings reveal asymmetric attention to various components of the predictive digital twin’s system. There is a relatively greater body of research on IoT and DT, representing 43 and 47%, respectively. In contrast, direct research on the use of AI for net-zero solutions constitutes only 10%. Similarly, the findings underscore the necessity of integrating these three technologies to develop predictive digital twin solutions for carbon emission prediction.

The results indicate that there is a clear need for more case studies investigating the use of large-scale IoT networks to collect carbon data from buildings and construction sites. Furthermore, the development of advanced and precise AI models is imperative for predicting the production of renewable energy sources and the demand for housing.

This paper makes a significant contribution to the field by providing a strong theoretical foundation. It also serves as a catalyst for future research within this domain. For practitioners and policymakers, this paper offers a reliable point of reference.

This study investigates the asymmetric influence of renewable energy consumption (REC) and trade openness (TO) in the pathway of decarbonizing of Indian economy.

By exploiting fifty years of annual time series data spanning from 1970 to 2019 with the augmentation of nonlinear autoregressive distributed lag technique with the consideration of GDP and industry value added (IVA) as control variables.

Our This research not only demonstrates the asymmetric association among the employed variables but also shows that negative shock to REC stimulates emissions, where as positive shock on the same policy variable promotes environmental quality improvement. Negative shock to TO is found to be associated with the corresponding increase of environmental quality, but the positive shock on the same intensifies environmental pollution. IVA is also found to be associated with intensifying environmental squalor. In addition, the research provides the empirical evidence of existence of “EKC” hypothesis in India as long-run coefficient associated with GDP looks smaller than short-run coefficient of GDP.

It was difficult to include may other causal variables due to nonavailability of data pertaining to those variables.

Moreover, some policy guidelines have also been recommended for India at the end that may aid India to achieve net zero emissions by 2070.

This is an original research paper carried out by the authors and has not yet been submitted elsewhere.

This study aims to conduct the domain mapping of consumer behaviour research in the context of solar energy. The study can help in understanding the intellectual structure, evolution of keywords and key research producers (at the author, institutional and source level) related to the domain of solar energy consumer research.

This study uses R-studios’ bibliometrix package for analysing the bibliographical data collected from the Scopus database. Analysis has been conducted at the descriptive level (summary, author, institution and source) and analytical level (co-citation analysis, co-occurrence analysis, thematic maps and historiography).

This study finds out the most relevant authors, institutions and sources using criteria such as production, citations and H-index. Relevant research clusters have been identified using the clustering of authors, co-citations and keywords. Thematic mapping has identified the basic and motor themes. Historical citation analysis shows the direct linkage of previous studies. Overall, this study reports the most relevant bibliometric indicators in the domain of solar energy consumer research.

Identified patterns can help policymakers, business experts, social marketers and energy conservation organisations to study consumer behaviour.

Thiis bibliometric study can effectively assess sustainable development goals and suggest improved action plans.

This study examined bibliometric analysis in solar energy products (SEPs), recognised varied domains of research work on consumers’ intention to purchase solar household products and mapped them into six groups. This study provides an overview of 40 years of research on consumer behaviour towards SEPs and discusses its findings to identify the research gap.

Egypt has set plans to transform into a green economy which requires major reforms in the waste sector as one of the most vital sectors crucial for this transformation. This study aims at inspecting the current status of the Egyptian waste sector to highlight the major policy reforms needed. Furthermore, it assesses the economic viability of establishing waste-to-energy (WtE) projects under the current regulations that govern the sector.

The study employed an inductive analytical approach to scrutinize the institutional and regulatory framework of the waste and WtE sectors. Furthermore, a novel techno-economic analysis was conducted to assess the profitability of a WtE plant that employs moving grate incineration technology.

The analysis of the waste sector revealed its deteriorating state and the dire need for immediate restructuring through more stringent regulations to establish an integrated waste management system (IWMS) that incorporates WtE technologies as well as a number of corrective actions that would help enhance the sector. Additionally, the techno-economic analysis revealed the need to amend the current WtE regulation to comprise a gate fee as an indispensable revenue stream for WtE projects.

This study is one of a few studies that uses a new technique of analysis to explore the potential role that WtE projects can play in Egypt as a part of an IWMS that aims at transforming the waste sector into a resource sector while providing a renewable and sustainable source of energy.

The energy efficiency of buildings has been emphasized along with the continual development in the building and construction sector that consumes a significant amount of energy. To this end, the purpose of this research paper is to forecast energy consumption to improve energy resource planning and management.

This study proposes the application of the convolutional neural network (CNN) for estimating the electricity consumption in the Grey Nuns building in Canada. The performance of the proposed model is compared against that of long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks. The models are trained and tested using monthly electricity consumption records (i.e. from May 2009 to December 2021) available from Concordia’s facility department. Statistical measures (e.g. determination coefficient [R²], root mean squared error [RMSE], mean absolute error [MAE] and mean absolute percentage error [MAPE]) are used to evaluate the outcomes of models.

The results reveal that the CNN model outperforms the other model predictions for 6 and 12 months ahead. It enhances the performance metrics reported by the LSTM and MLP models concerning the R², RMSE, MAE and MAPE by more than 4%, 6%, 42% and 46%, respectively. Therefore, the proposed model uses the available data to predict the electricity consumption for 6 and 12 months ahead. In June and December 2022, the overall electricity consumption is estimated to be 195,312 kWh and 254,737 kWh, respectively.

This study discusses the development of an effective time-series model that can forecast future electricity consumption in a Canadian heritage building. Deep learning techniques are being used for the first time to anticipate the electricity consumption of the Grey Nuns building in Canada. Additionally, it evaluates the effectiveness of deep learning and machine learning methods for predicting electricity consumption using established performance indicators. Recognizing electricity consumption in buildings is beneficial for utility providers, facility managers and end users by improving energy and environmental efficiency.

This paper aims to emphasize economic complexity, tourism, information and communication technology (ICT), renewable energy consumption and foreign direct investment (FDI) as the determinants of carbon emissions.

These economies rely on the tourism sector, and Asian countries rank among the top tourism economies worldwide in terms of tourism receipts. This study uses a series of empirical estimators, i.e. cross-sectional augmented auto-regression distributive lag and panel cointegration, to validate the main hypotheses.

The econometric results confirm an inverted U-shaped association between economic complexity and carbon emissions, validating the economic complexity index induced environment Kuznets curve hypothesis for the selected Asian economies.

Finally, the empirical results admit articulating some imperative policy suggestions to attain a sustainable environment on behalf of outcomes.

Furthermore, ICT and renewable energy consumption are environment-friendly indicators, while FDI and the international tourism industry increase environmental pressure in selected countries. In addition, this study also explores the interaction between renewable energy and ICT with FDI and their effects on carbon emissions. Interestingly, both interaction terms positively respond to the environmental correction process.

Because ICT with FDI may not reduce environmental pollution unless the energy used in FDI projects is greener. Moreover, in Asian economies, industrial and other sectors could increase environmental quality via the role of ICT in FDI.

修正亚洲前 8 大经济体的旅游环境库兹涅茨曲线假设：ict 和可再生能源消耗的作用

这些经济体依赖旅游业, 就旅游收入而言, 亚洲国家在全球旅游经济体中名列前茅。本研究使用一系列经验估计量, 即 CS-ARDL 和面板协整来验证我们的主要假设。

本文强调经济复杂性、旅游、信息和通信技术 (ICT)、可再生能源消费和外国直接投资 (FDI) 作为碳排放的决定因素

计量经济学结果证实了经济复杂性与碳排放之间的倒 U 型关联, 验证了 ECI 对选定亚洲经济体的环境库兹涅茨曲线 (EKC) 假设。

最后, 实证结果承认阐明了一些必要的政策建议, 以代表结果实现可持续环境。

此外, 信息通信技术和可再生能源消耗是环境友好型指标, 而外国直接投资和国际旅游业增加了选定国家的环境压力。此外, 本研究还探讨了可再生能源和 ICT 与外国直接投资之间的相互作用及其对碳排放的影响。有趣的是, 这两个交互项都对环境校正过程做出了积极响应。

ICT 与 FDI 可能不会减少环境污染, 除非 FDI 项目中的能源使用更环保。此外, 在亚洲经济体中, 工业和其他部门可以通过 ICT 在 FDI 中的作用提高环境质量。

环境库兹涅茨曲线; 外商直接投资;信息和通信技术; 可再生能源;旅游;亚洲主要旅游经济体

文章类型： 研究型论文

With the rise of mandating climate-related disclosures (CRD), this paper aims to investigate how energy and agriculture markets are exposed to climate disclosure risk.

Using the multivariable simultaneous quantile regression and data from 1 January 2017 to 29 February 2024, the authors examine daily and monthly responses of energy and agriculture markets to climate disclosure risk, energy risk, market sentiment, geopolitical risk and economic policy risk. The sample covers the global market, Australia, Canada, European Union (EU), Hong Kong, Japan, New Zealand, Singapore, the UK and the USA.

The results show that climate disclosure risk creates both positive and negative shocks in the energy and agriculture markets, and the impacts are asymmetric across quantiles in different economies. The higher the climate disclosure risk, the greater impact of crude oil future on the energy sector in North America (Canada and the USA) and Europe (EU and the UK), but no greater effects in Asia Pacific (Australia, New Zealand and Singapore). The agriculture sector can hedge against economic policy and geopolitical risks, but it is highly exposed to climate disclosure and energy risks.

This study timely contributes to the modest literature on the asymmetric effects of climate disclosure risk on the energy and agriculture markets at the global and national levels. The findings offer practical implications for policymakers and investment practitioners in understanding financial effects of mandating CRD to diversify risks depending upon market conditions and policy uncertainty.