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Urbanization is driving the growth of China’s carbon footprint. It’s important to investigate what factors, how and to what extent, affect carbon footprints embedded in various categories of rural and urban households’ consumption.

We employ an environmental extended input-output model to assess and compare the rural-urban household carbon footprints and perform a multivariant regression analysis to identify the varying relationships of the determinants on rural and urban household carbon footprints based on the panel data of Chinese households from 2012 to 2018.

The results show evidence of urbanity density effect on direct carbon footprints and countervailing effect on indirect carbon footprints. The old dependency ratio has no significant effect on rural family emissions but has a significantly negative effect on urban direct and indirect carbon footprints. A higher child dependency ratio is associated with less rural household carbon emissions while the opposite is true for urban households. Taking advantage of recycled fuel saves direct carbon emissions and this green lifestyle benefits urban households more by saving more carbon emissions. There is a positive relationship between consumption structure ratio and direct carbon footprints while a negative relationship with indirect carbon footprints and this impact is less significant for urban households. The higher the price level of water, electricity and fuel, the lower the rural household’s direct carbon footprints. Private car ownership consistently augments household carbon footprints across rural and urban areas.

This paper provides comprehensive findings to understand the relationships between an array of determinants and China’s rural-urban carbon emissions, empowering China’s contribution to the global effort on climate mitigation.

The purpose of this paper is to present a comprehensive analysis of the carbon footprint of the Delft University of Technology (TU Delft), including direct and indirect emissions from utilities, logistics and purchases, as well as a discussion about the commonly used method. Emissions are presented in three scopes (scope 1 reports direct process emissions, scope 2 reports emissions from purchased energy and scope 3 reports indirect emissions from the value chain) to identify carbon emission hotspots within the university’s operations.

The carbon footprint was calculated using physical and monetary activity data, applying a process and economic input-output analysis.

TU Delft’s total carbon footprint in 2018 is calculated at 106 ktCO₂eq. About 80% are indirect (scope 3) emissions, which is in line with other studies. Emissions from Real estate and construction, Natural gas, Equipment, ICT and Facility services accounted for about 64% of the total footprint, whereas Electricity, Water and waste-related carbon emissions were negligible. These findings highlight the need to reduce universities’ supply chain emissions.

A better understanding of carbon footprint hotspots can facilitate strategies to reduce emissions and finally achieve carbon neutrality. In contrast to other work, it is argued that using economic input-output models to calculate universities’ carbon footprints is a questionable practice, as they can provide only an initial estimation. Therefore, the development of better-suited methods is called for.

As net-zero pledges gain momentum globally, more and more accommodation businesses seek to quantify their carbon emissions. Building on Chan (2021), this study aims to explore what drives Australian accommodation providers to measure the carbon footprint of their businesses and what barriers hinder them from doing so.

Empirical data were collected by conducting ten semi-structured interviews with owners, senior executives, consultants, certification bodies and hotel management companies. The set of interviews represented different segments of the hotel industry and various accommodation types. Data were analysed with thematic analysis.

The major drivers for adopting carbon footprint analysis are as follows: the analysis being perceived as an important contribution to a company's corporate responsibility, the owner or manager's environmental concern, the assessment being a requirement for obtaining an eco-certification and the business benefits associated with implementing the initiative. The major barriers hindering adoption include the following: difficulties with data gathering, the lack of a standard methodology, a lengthy decision-making process and a lack of resources.

Based on the empirical findings and three theories on ecological responsiveness, this study develops a conceptual framework for implementing carbon footprint analysis in the accommodation context and recommends strategies to increase the adoption of carbon footprint analysis.

This study responds to Chan and Hsu's (2016) call for further research on carbon footprint in the hotel context and represents the first attempt to explore the drivers and barriers specifically associated with implementing carbon footprint analysis in the accommodation sector.

It is very crucial to evaluate the suitability of food services from an environmental and economic point of view to design sustainable food menus. This study aims to analyse the food menus in a Turkish university refectory concerning sustainable nutrition and waste management and compare them with a proposed sustainable food menu.

The study examined lunch menus served in December and February 2021–2022 at Istanbul Medipol University refectory for a total of 20 days, considering the nutrient-rich food index (NRF 9.3), waste amount, food cost, water and carbon footprint parameters of the meals.

Comparing the December menu with the suggested sustainable December menu showed a significant reduction in carbon and water footprint (p = 0.001), food cost (p = 0.001) and NRF 9.3 score (p = 0.001). When February and the suggested sustainable February menu were compared, there was a significant decrease in carbon and water footprint (p = 0.001), food cost (p = 0.005) and NRF 9.3 score (p = 0.001). December and February menus had higher NRF 9.3 score compared to the sample sustainable menus, and the amounts of saturated fat, added sugar and sodium were also high in these menus.

The study revealed that university cafeteria menus are incompatible with sustainable nutrition. The findings can significantly contribute to improving the sustainability of meals and food services by minimizing the water and carbon footprint of menus.

With the change in climate and increased pollution, there has been a need to reduce environmental carbon emissions. This research aims to develop a framework for reducing environmental carbon footprints to improve business performance.

This study uses Scientific Procedures and Rationales for the Systematic Literature Reviews (SPAR-4-SLR) approach. Articles are searched in the Scopus database using various keywords and their combinations. It resulted in 651 articles initially. After applying different screening criteria, 61 articles were considered for the final study.

This study provided four themes and sub-themes within each category. This research also used theories, methodologies and context (TMC) framework to provide future research questions. This study used the antecedents, decisions and outcomes (ADO) framework for synthesising the findings. The ADO framework will help to achieve carbon neutrality and improve firms' supply chain (SC) performance.

This study provides theoretical implications by highlighting the various theories that can be used in future research. This study also states the practical implications for the achievement of carbon neutrality by the firms.

This study contributes to the literature linking carbon neutrality with business performance.

The purpose of this paper is to determine the effect of the industrial sector, renewable energy consumption and nonrenewable energy consumption in Indonesia on the ecological footprint from 1990 to 2020 in the short and long term.

This paper uses vector error correction model (VECM) analysis to examine the relationship in the short and long term. In addition, the impulse response function is used to enable future forecasts up to 2060 of the ecological footprint as a measure of environmental degradation caused by changes or shocks in industrial value-added, renewable energy consumption and nonrenewable energy consumption. Furthermore, forecast error decomposition of variance (FEVD) analysis is carried out to predict the percentage contribution of each variable’s variance to changes in a specific variable. Granger causality testing is used to enhance the analysis outcomes within the framework of VECM.

Using VECM analysis, the speed of adjustment for environmental damage is quite high in the short term, at 246%. This finding suggests that when there is a short-term imbalance in industrial value-added, renewable energy consumption and nonrenewable energy consumption, the ecological footprint experiences a very rapid adjustment, at 246%, to move towards long-term balance. Then, in the long term, the ecological footprint in Indonesia is most influenced by nonrenewable energy consumption. This is also confirmed by the Granger causality test and the results of FEVD, which show that the contribution of nonrenewable energy consumption will be 10.207% in 2060 and will be the main contributor to the ecological footprint in the coming years to achieve net-zero emissions in 2060. In the long run, renewable energy consumption has a negative effect on the ecological footprint, whereas industrial value-added and nonrenewable energy consumption have a positive effect.

For the first time, value added from the industrial sector is being used alongside renewable and nonrenewable energy consumption to measure Indonesia’s ecological footprint. The primary cause of Indonesia’s alarming environmental degradation is the industrial sector, which acts as the driving force behind this issue. Consequently, this contribution is expected to inform the policy implications required to achieve zero carbon emissions by 2060, aligned with the G20 countries’ Bali agreement of 2022.

This study examines how carbon tools, including carbon accounting and management tools, can be created, used, modified and linked with other traditional management controls to materialise and effectuate organisations’ response strategies to multiple interacting logics in carbon management and the role of sustainability managers in these processes.

This study utilises the construct of accounting toolmaking, which refers to practices of adopting, adjusting and reconfiguring accounting tools to unfold how carbon tools are used as means to materialise responses to multiple interacting carbon management logics. It embraces a field study approach, whereby 38 sustainability managers and staff from 30 organisations in New Zealand were interviewed.

This study finds that carbon toolmaking is an important means to materialise and effectuate organisations’ response strategies to multiple interacting carbon management logics. Four response strategies are identified: separation, selective coupling, combination and hybridisation. Adopting activity involves considering the additionality, detailing, localising and cascading of carbon measures and targets and their linkage to the broader carbon management programme. In adjusting carbon tools, organisations adapt the frequency and orientation of carbon reporting, intensity of carbon monitoring and breadth of carbon information sharing. Through focusing on either procedural sequencing, assimilating, equating or integrating, toolmaking reconfigures the relationship between carbon tools and traditional management control systems. Together, these three toolmaking activities can be configured differently to construct carbon tools that are fit for purpose for each response strategy. These activities also enact certain roles on sustainability managers in the process of representing, communicating and/or transferring carbon information knowledge, which also facilitate different response strategies.

The study demonstrates the various carbon toolmaking practices that allow organisations to handle the multiple interacting logics in carbon management. The findings provide suggestions for organisations on how to adopt, adjust and reconfigure carbon tools to better embed the ecological logic in organisations’ strategies and operations.

The authors identify how carbon toolmaking materialises and effectuates organisations’ responses to multiple interacting logics in carbon management.

The purpose of this research is to test the effectiveness of integrating Grasshopper 3D and measuring attractiveness by a categorical based evaluation technique (M-MACBETH) for building energy simulation analysis within a virtual environment. Set of energy retrofitting solutions is evaluated against performance-based criteria (energy consumption, weight and carbon footprint), and considering the preservation of the cultural value of the building, its architectural and spatial configuration.

This research addresses the building energy performance analysis before and after the design of retrofitting solutions in extreme climate environments (2030–2100). The proposed model integrates data obtained from an advanced parametric tool (Grasshopper) and a multi-criteria decision analysis (M-MACBETH) to score different energy retrofitting solutions against energy consumption, weight, carbon footprint and impact on architectural configuration. The proposed model is tested for predicting the performance of a traditional timber-framed dwelling in a historic parish in Lisbon. The performance of distinct solutions is compared in digitally simulated climate conditions (design scenarios) considering different criteria weights.

This study shows the importance of conducting building energy simulation linking physical and digital environments and then, identifying a set of evaluation criteria in the analysed context. Architects, environmental engineers and urban planners should use computational environment in the development design phase to identify design solutions and compare their expected impact on the building configuration and performance-based behaviour.

The unavailability of local weather data (EnergyPlus Weather File (EPW) file), the high time-resource effort, and the number/type of the energy retrofit measures tested in this research limit the scope of this study. In energy simulation procedures, the baseline generally covers a period of thirty, ten or five years. In this research, due to the fact that weather data is unavailable in the format required in the simulation process (.EPW file), the input data in the baseline is the average climatic data from EnergyPlus (2022). Additionally, this workflow is time-consuming due to the low interoperability of the software. Grasshopper requires a high-skilled analyst to obtain accurate results. To calculate the values for the energy consumption, i.e. the values of energy per day of simulation, all the values given per hour are manually summed. The values of weight are obtained by calculating the amount of material required (whose dimensions are provided by Grasshopper), while the amount of carbon footprint is calculated per kg of material. Then this set of data is introduced into M-MACBETH. Another relevant limitation is related to the techniques proposed for retrofitting this case study, all based on wood-fibre boards.

The proposed method for energy simulation and climate change adaptation can be applied to other historic buildings considering different evaluation criteria and context-based priorities.

Context-based adaptation measures of the built environment are necessary for the coming years due to the projected extreme temperature changes following the 2015 Paris Agreement and the 2030 Agenda. Built environments include historical sites that represent irreplaceable cultural legacies and factors of the community's identity to be preserved over time.

This study shows the importance of conducting building energy simulation using physical and digital environments. Computational environment should be used during the development design phase by architects, engineers and urban planners to rank design solutions against a set of performance criteria and compare the expected impact on the building configuration and performance-based behaviour. This study integrates Grasshopper 3D and M-MACBETH.

The authors target the interrelationships between non-fungible tokens (NFTs), decentralized finance (DeFi) and carbon allowances (CA) markets during 2021–2023. The recent shift of crypto and DeFi miners from China (the People's Republic of China, PRC) green hydro energy to dirty fuel energies elsewhere induces investments in carbon offsetting instruments; this is a backdrop to the authors’ investigation.

The quantile vector autoregression (VAR) approach is employed to examine extreme-quantile-connectedness and spillovers among the NFT Index (NFTI), DeFi Pulse Index (DPI), KraneShares Global Carbon Strategy ETF price (KRBN) and the Solactive Carbon Emission Allowances Rolling Futures Total Return Index (SOLCARBT).

At bull markets, DPI is the only consistent net shock transmitter as NFTI transmits innovations only at the most extreme quantile. At bear markets, KRBN and SOLCARBT are net shock transmitters, while NFTI is the only consistent net shock receiver. The receiver-transmitter roles change as a function of the market conditions. The increases in the relative tail dependence correspond to the stress events, which make systemic connectedness augment, turning market-specific idiosyncratic considerations less relevant.

The shift of digital asset miners from the PRC has resulted in excessive fuel energy consumption and aggravated environmental consequences regarding NFTs and DeFi mining. Although there exist numerous studies dedicated to CA trading and its role in carbon print reduction, the direct nexus between NFT, DeFi and CA has never been addressed in the literature. The originality of the authors’ research consists in bridging this void. Results are valuable for portfolio managers in bull and bear markets, as the authors show that connectedness is more intense under such conditions.

Businesses must now track the complicated supply chains of their products, which involve different manufacturers and suppliers. However, because supply chains are scattered across multiple countries and involve many institutions, it becomes an overwhelming practical challenge to ensure transparent recording and reporting of greenhouse gas emissions. The myriad issues necessitate a technological solution that will improve supply chain transparency, assist in managing carbon assets and allow all parties to obtain credible information on carbon output. As a potential solution, this study offers a unique architecture that effectively combines “blockchain technology” with the carbon supply chain of a multi-institution business network.

This research and proposed framework are based on publicly available reports on carbon emissions tracking, sustainability, carbon trade and emerging blockchain technologies. The authors also interviewed industry experts to obtain their input and feedback.

Businesses must support the pledges made by their respective governments towards meeting the objectives of the Paris Agreement. Although the emissions trading system encourages businesses to move in this direction, it can be challenging for them to efficiently manage their carbon assets owing to issues such as lack of standardised methods for tracking emissions across suppliers and manufacturers and the fragmentation of carbon markets. The carbon supply chain can maintain a record of the chronological flow of carbon emissions and eventually of all carbon assets by integrating a centralised ledger system based on blockchain technology.

Global warming, climate change and carbon emissions are among humanity’s pressing problems today. To achieve net zero emissions by the middle of the 21st century, emissions must be drastically reduced. Global supply chains have a crucial role to play in this context. This article provides a blockchain-based technology framework for carbon emissions visibility and tracking. The authors believe such a platform will provide critical visibility and tracking support to globally dispersed supply chains, moving a step closer towards carbon emissions control and net zero operations.