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Buildings require significant energy, and meeting energy demands is becoming exceedingly challenging. Energy demand reduction goals are now prioritised as the demand is rising. Energy-saving improvements and opportunities can be provided if enough information is provided through building energy benchmarking. The study focuses on developing a framework for benchmarking the energy efficiency of residential buildings.

This study applied multiple linear regression analysis to analyse the energy use of residential buildings and establish energy benchmarks. Over 2000 data from Jaipur city were surveyed, and regression analysis was done on 1527 datasets after fundamental statistical analysis. The research considered the significant energy used by household appliances and placed a greater emphasis on end-use appliances.

The comparison of the developed framework with the standard rating plan was carried out to evaluate the accuracy of the benchmarks. The validation of the model determines the gap between the predicted and actual value of the building energy. The recommendations were made for organisations and policymakers to employ multiple or combinations of methods to assess the reliability of the developed benchmark framework.

Policymakers may promote awareness campaigns encouraging homeowners to consume less energy and make buildings more energy efficient. This technique may be applied worldwide with the proper and suitable adjustments and information provided.

To our knowledge, India needs residential building energy benchmarking framework studies. In addition, a new framework based on Composite Indicators was implemented to overcome the scepticism of the EPI/BPI or floor-based approach held by several academics and to offer energy benchmarking for residential buildings.

This paper aims to examine the performance efficiency of 56 real estate assets within the rental sector in the UAE to evaluate the relative operation efficiency in relation to revenue generation.

The data envelopment analysis (DEA) approach was used to measure the relative operational efficiency of the studied assets in relation to the revenue performance. This method could produce a more informed and balanced approach to performance measurement.

The outcomes show that scores of efficiencies ranging from 7% to 99% in some of the models. The results showed that on average buildings are 75% relatively less efficient in maintenance, in term of revenue generation, than the benchmark set. Likewise, on average, the inefficient buildings are 60% relatively less efficient in insurance. Result also shows that 95% of the building assets in the sample are by and large operating at decreasing returns to scale. This implies that managers need to considerably reduce the operational resources (input) to improve the levels of revenue.

This study recommends that the FM operational variables that were found to inefficiently contribute to the revenue should be re-examined to test the validity of the findings. This is necessary before generalising or interpolating the results that are presented in this study.

The information obtained about operational performance can help FM managers to understand which improvements in the productivity of inefficient FM resources are required, providing insight into how to reduce operating costs and increase revenue.

This paper adds value in using new FM operational parameters to evaluate the efficiency of the performance of built assets.

The building sector contributes one-third of the energy-related carbon dioxide globally. Therefore, framing appropriate energy-related policies for the next decades becomes essential in this scenario to realize the global net-zero goals. The purpose of the proposed study is to evaluate the impact of the widespread adoption of such guidelines in a building community in the context of mixed-mode buildings.

This study decentralizes the theme of improving the energy efficiency of the national building stock in parcels by proposing a community-based hybrid bottom-up modelling approach using urban building energy modelling (UBEM) techniques to analyze the effectiveness of the community-wide implementation of energy conservation guidelines.

In this study, the UBEM is developed and validated for the 14-building residential community in Mumbai, India, adopting the framework. Employing Energy Conservation Building Code (ECBC) compliance on the UBEM shows an energy use reduction potential of up to 15%. The results also reveal that ECBC compliance is more advantageous considering the effects of climate change.

In developing countries where the availability of existing building stock information is minimal, the proposed study formulates a holistic framework for developing a detailed UBEM for the residential building stock from scratch. A unique method of assessing the actual cooling load of the developed UBEM is presented. A thorough sensitivity analysis approach to investigate the effect of cooling space fraction on the energy consumption of the building stock is presented, which would assist in choosing the appropriate retrofit strategies. The proposed study's outcomes can significantly transform the formulation and validation of appropriate energy policies.

Energy consumption in existing office buildings has been growing in parallel with the rise in occupant energy demand. As a result, many building owners have given smart retrofits (SRs) a higher priority. However, the utilisation of suitable SRs from a range of SRs has become a challenging task. The purpose of this paper is to develop a decision-making model to select the most suitable SRs for conventional office buildings and form a set of benchmarks for assessing the performance of SRs.

A qualitative approach with six case studies was used. Content analysis was carried out using NVivo to explore the factors considered for the selection of SR techniques. A decision-making model for selecting SRs in Sri Lankan office buildings was proposed. SR performance benchmarks were developed by referring to established standards and studies done in tropical office buildings.

Out of 18 identified SRs from literature, fan cycling, ventilation control and LED luminaires have been recognised as commonly used SRs in Sri Lankan office buildings. Analysis showed that HVAC retrofits saved more energy, while lighting retrofits could be easily implemented in existing buildings. The proposed decision-making model can explore further improvements to enhance the performance of SRs.

The selection of SRs is a comprehensive decision-making process. Metrics were established to benchmark the performance of SRs. The proposed model offers a tool for building owners and facility managers to optimise facility operations.

The rapid development and high penetration of digitalization have triggered profound changes in the energy sector. The purpose of this study is to integrate the government digital transformation into the analysis framework and discuss its impact on urban energy efficiency and its realization mechanism.

Using the “Information Benefit Pilot City” (IBC) policy as a quasi-natural experiment, and drawing on data from 285 prefecture-level cities in China from 2008 to 2019, this paper discusses how digital government affects urban energy efficiency by using difference-in-differences (DID).

The results show that digital governance significantly improves energy efficiency, and this conclusion remains reliable even after a series of robustness tests, endogeneity processing and sensitivity analysis. Heterogeneity results show that resource-based, eastern, high economic development level and high urbanization rate city digital government construction are more conducive to improving energy efficiency. The mediating effect shows that the influence mechanism of digital government on energy efficiency mainly includes reducing carbon emission, promoting green technology innovation and attracting talents.

(1) From the perspective of government digital transformation, this study supplements the way to improve energy efficiency and also expands the social dividend of government governance transformation. (2) Through quasi-experimental analysis of IBC policy, this paper solves the problem of difficulty in quantifying the government's digital transformation indicators. (3) The impact heterogeneity and realization mechanism are further discussed and the specific ways of digital government's impact on energy efficiency are revealed.

As one of the most energy-intensive sectors, the manufacturing industry is strongly affected by current economic, ecological and political issues and is increasingly looking to reduce its energy consumption through effective management. One important component of energy management is monitoring, which can be improved by using the Internet of Things (IoT). This study aims to identify the factors behind the adoption of IoT energy monitoring systems in manufacturing industries and IoT’s impact on consumption in this context. Moreover, this study explores what constitutes successful implementation and develops recommendations on how companies can best use the generated energy-saving potential.

For this purpose, an exploratory, inductive research approach is taken in which five semi-structured interviews with IoT energy-monitoring platform providers located in Germany are conducted and additional documents about these platforms are analysed.

Based on these interviews and documents, this study defines the relevant factors involved in the increased adoption of IoT energy monitoring systems as desired cost reduction, regulatory requirements and customer expectations. Once implemented, a company’s energy efficiency potential strongly depends on its individual situation; however, the transparency characteristic of IoT supports managers in deriving effective energy-saving measures. Additionally, the used efficiency potential is influenced by the level of data collection and organisational capabilities.

To the best of the authors’ knowledge, as the first study, this research combines findings from the energy management and IoT research streams and places them into an organisational context. The application of the fit-viability model as an overarching framework enhances the theoretical contributions of this study. Moreover, focusing on Germany and its substantial industrial sector enables the gathering of important insights and the analysis of the specific use case of industrial IoT, which yields new findings for practitioners as well.

This study aims to evaluate the quality of the energy efficiency disclosures made by Australian cities. As cities are significant energy users, and energy use is a crucial source of greenhouse gas emissions, energy efficiency initiatives can play an essential role in addressing climate change. Yet, little is understood about the energy efficiency disclosures being made.

The authors developed an original energy efficiency disclosure index to assess the reporting quality of the eight largest Australian cities. The websites of these cities were analysed for information on energy efficiency measures from December 2018 to June 2019. Annual reports, environmental reports, climate action plans and any other material related to energy plans were downloaded and then coded using the index.

While all cities provided energy efficiency information, little financial information was provided, limited forward-looking information was disclosed, key challenges were not disclosed, and each city provided energy efficiency disclosures differently. Collectively, these findings demonstrate that public accountability is limited.

An important implication is the need to standardise and improve cities’ energy efficiency reporting, especially concerning financial information. Cities, governments and the Carbon Disclosure Project (formerly the CDP) could achieve this, perhaps as part of the broader update of the CDP city-focused guidelines for greenhouse gas (GHG) reporting.

Although some studies on GHG reporting by cities have already been undertaken, including energy efficiency as part of their disclosure index, no study has focused on energy efficiency disclosures. The authors provide original insights concerning these practices. The study also provides an energy efficiency disclosure index that can be used in further research.

This study aims to investigate environmental efficiency based on energy change by using energy-related or nonenergy-related variables by reckoning with months and years as decision-making units (DMUs) for a hospital under radial and nonradial models.

The non-oriented slack-based measures (SBM)-data envelopment analysis (DEA) model considering desirable and undesirable outputs has been embraced in this study, where its obtained results were compared with the results of other DEA models are output-oriented SBM-DEA and Banker, Charnes, & Cooper-DEA. For this purpose, this research has used a data set covering the 2012–2018 period for a reference hospital, which includes energy-related and nonenergy-related variables.

The results demonstrate that environmental efficiency based on energy reached the highest level in the winter months, whereas the summer months have the lowest efficiency values arising from the increasing electricity consumption due to high cooling needs. According to results of the non-oriented SBM model, the month with the highest efficiency in all periods is January with a 0.936 average efficiency score, the lowest month is August with a 0.406 value.

This paper differs from other studies related to energy and environmental efficiencies in the literature with some aspects. First, to the best of the authors’ knowledge, this study is the first one that takes into account time periods (months and years) as (DMUs for a single organization. Second, this study investigates environmental nonefficiencies, which are derived from energy uses and factors affecting energy use.

For over 25 years, the United States Green Building Council (USGBC) has significantly influenced the US sustainable construction through its leadership in energy and environmental design (LEED) certification program. This study aims to delve into how Baton Rouge, Louisiana, fares in green building adoption relative to other US capital cities and regions.

The study leverages statistical and geospatial analyses of data sourced from the USGBC, among other databases. It scrutinizes Baton Rouge’s LEED criteria performance using the mean percent weighted criteria to pinpoint the LEED criteria most readily achieved. Moreover, unique metrics, such as the certified green building per capita (CGBC), were formulated to facilitate a comparative analysis of green building adoption across various regions.

Baton Rouge’s CGBC stands at 0.31% (C+), markedly trailing behind the frontrunner, Santa Fe, New Mexico, leading at 3.89% (A+) and in LEED building per capita too. Despite the notable concentration of certified green buildings (CGBs) within Baton Rouge, the city’s green building development appears to be in its infancy. Innovation and design was identified as the most attainable LEED benchmark in Baton Rouge. Additionally, socioeconomic factors, including education and income per capita, were associated with a mild to moderate positive correlation (0.25 = r = 0.36) with the adoption of green building practices across the capitals, while sociocultural infrastructure exhibited a strong positive correlation (r = 0.99).

This study is beneficial to policymakers, urban planners and developers for sustainable urban development and a reference point for subsequent postoccupancy evaluations of CGBs in Baton Rouge and beyond.

This study pioneers the comprehensive analysis of green building adoption rates and probable influencing factors in capital cities in the contiguous US using distinct metrics.

This study investigates how the initiatives and controversies related to environmental, social and governance (ESG) explain firm efficiency.

Firstly, this study applies data envelopment analysis with the epsilon-based measure to estimate the firm efficiency of 80 companies in the Chinese energy sector in 2022. This approach accounts for the diversity and relative importance of inputs and outputs from a multidimensional perspective. Secondly, this study regresses the variables of ESG initiatives and controversies on the estimated firm efficiency scores through a generalised additive model, which can capture nonlinear patterns.

This study finds that a) the samples have i) about 49% room for improvement in efficiently optimising their resources and business outcomes and ii) the highest scores in governance initiatives, followed by social initiative. b) 69% of them have controversy scores that are greater than the average value. c) A cluster analysis indicates that companies with higher social initiatives have higher firm efficiency than their counterparts. d) ESG initiatives and controversies are nonlinearly related to firm efficiency.

The findings have practical implications for policy makers and managers who prioritise ESG, particularly regarding (i) the need to examine firm performance from a multidimensional perspective, that is, to measure multiple inputs and outputs simultaneously, (ii) the nonlinearity of the nexus between ESG and efficiency in graphical forms, and (iii) the need to balance ESG initiatives and address ESG controversies.

This study integrates statistical approaches in examining and ensuring sustainable growth and efficiency within the Chinese energy sector and beyond.