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The term “carbon footprint” emerged during the early 2000s, but many hotels remain unaware of what they should do to implement a comprehensive programme to reduce carbon footprint despite having some environmental measures. This study aims to investigate the barriers to reducing hotel carbon footprint and to explore why many hotel managers remain bystanders.

In-depth semi-structured interviews were conducted with hotel executives to understand what hinders hotels’ implementation of comprehensive programmes to reduce their carbon footprint. The NVivo 11 software package was used to organise data and code the transcribed interviews to identify patterns and themes.

The findings identified several main barriers. They were (1) a lack of understanding, (2) a lack of owner initiative, (3) difficulty with measurements, (4) a lack of stakeholder coordination and support, (5) a lack of a strong mediator, (6) balancing interests and (7) risky investment. The findings of this study suggest some specific strategies for overcoming these barriers.

The study sample was restricted to the Hong Kong hotel executives interviewed; therefore, the findings will not reflect the full picture of managerial perceptions. Drawing on the foundations laid by this study, researchers could collect quantitative data from hotels in other countries to conduct a cross-cultural study.

Very few studies have investigated barriers to carbon-footprint reduction programmes. Specifically, none have been published in the hotel environmental management literature. This study represents a preliminary step towards understanding the barriers that prevent hotels from implementing the programmes.

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.

This study aims to evaluate the nutrient profile, carbon footprint and water footprint of one-month menus presented in five hospitals in Turkey and compare their environmental impacts with that of the Mediterranean diet.

The energy and nutrient content of menus were compared with recommendations of the Turkey Dietary Guidelines (TUBER) 2022. Nutrient profiles of hospital menus were evaluated using Nutrient Rich Food 9.3 (NRF 9.3) and SAIN-LIM models. The carbon and water footprints of the menus were calculated and compared with those of the Mediterranean diet.

Menus’ energy and nutrient content did not conform with TUBER 2022. The SAIN-LIM score of Hospital A (5.7 ± 1.1) was significantly higher than that of Hospitals C (4.8 ± 0.7, p = 0.001) and E (5.1 ± 0.7, p = 0.025). The carbon footprint of Hospital A was significantly lower (2.6 ± 0.3 kg CO₂ eq/person/day) and that of Hospital D (4 ± 0.9 kg CO₂ eq/person/day) was significantly higher than those of others (p < 0.001). While other menus were similar (p > 0.05), the water footprint of Hospital A was significantly lower (3.5 ± 0.7 L/kg, p < 0.001). In addition, if the menus were suitable for the Mediterranean diet, a reduction of 2.2–23.4% in the carbon footprint and 37.5–58.6% in the water footprint could be achieved. Moreover, menus’ carbon and water footprints were negatively correlated with NRF 9.3 and SAIN-LIM scores.

The primary aim should be to ensure that the planned menus follow the dietary guidelines. In addition, it is an undeniable fact that sustainable nutrition is a complex process with many dimensions. However, it seems possible to improve the nutrient profiles of the menus and reduce their environmental footprint with minor changes to be made in food services.

It seems possible to improve the nutrient profiles of the menus and reduce their environmental footprint with minor changes. For both health and environmental impacts, food services should switch to menus suitable for the Mediterranean diet.

The findings provide new insights into hospital menus’ quality and environmental impact.

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.

The purpose of this paper is to propose a low carbon culture (LCC) adoption model for gaining the right carbon capabilities by integrating the dimensions of flexibility or control and external or internal of competing values framework (CVF) with that of level of carbon emission (LCE).

This study reviewed literature related to low carbon supply chain, CVF and carbon capabilities to synthesize currently available frameworks for assessing culture and carbon-related insights. Based on these insights, this study proposes the carbon culture adoption model and presents some research propositions.

This study has extended categorization of culture suggested in CVF from four categories to eight distinct categories by adding “LCE” as a third dimension. The new categories of carbon culture are: “Red,” “Antagonist,” “Obligatory,” “Early Adopter,” “Follower,” “Transitive,” “Pragmatist” and “Green.” This categorization of organizations would help in selecting appropriate low carbon practices (LCPs).

This study presents purely conceptual framework with some research propositions which needs to be empirically tested.

Organizations can formulate right policies for low carbon capabilities based on the LCC of their supply chain.

With increasing awareness about environment across stakeholders, organizations around the world are under pressure to reduce their carbon footprints. The extent of reduction in carbon footprints depends on the right capabilities across the supply chain which in turn depends on selection of the right combination of LCPs based on the supply chain culture.

The purpose of this study is to model a vehicle routing problem with integrated picking and delivery under carbon cap and trade policy. This study also provides sensitivity analyses of carbon cap and price to the total cost.

A mixed integer linear programming (MILP) model is formulated to model the vehicle routing with integrated order picking and delivery constraints. The model is then solved by using the CPLEX solver. Carbon footprint is estimated by a fuel consumption function that is dependent on two factors, distance and vehicle speed. The model is analyzed by considering 10 suppliers and 20 customers. The distance and vehicle speed data are generated using simulation with random numbers.

Significant amount of carbon footprint can be reduced through the adoption of eco-efficient vehicle routing with a marginal increase in total transportation cost. Sensitivity analysis indicates that compared to carbon cap, carbon price has more influence on the total cost.

The model considers mid-sized problem instances. To analyze large size problems, heuristics and meta-heuristics may be used.

This study provides an analysis of carbon cap and price model that would assist practitioners and policymakers in formulating their policy in the context of carbon emissions.

This study provides two significant contributions to low carbon supply chain management. First, it provides a vehicle routing model under carbon cap and trade policy. Second, it provides a sensitivity analysis of carbon cap and price in the model.

The purpose of this paper is to emphasize the importance of a life cycle approach in facilities management practices to reduce the carbon footprint of built facilities. A model to holistic life cycle energy and carbon reduction is also proposed.

A literature-based discovery approach was applied to collect, analyze and synthesize the results of published case studies from around the globe. The energy use results of 95 published case studies were analyzed to derive conclusions.

A comparison of energy-efficient and conventional facilities revealed that decreasing operating energy may increase the embodied energy components. Additionally, the analysis of 95 commercial buildings indicated that nearly 10 per cent of the total US carbon emissions was influenced by facilities management practices.

The results were derived from case studies that belonged to various locations across the globe and included facilities constructed with a variety of materials.

The proposed approach to holistic carbon footprint reduction can guide facility management research and practice to make meaningful contributions to the efforts for creating a sustainable built environment.

This paper quantifies the extent to which a facilities management professional can contribute to the global efforts of reducing carbon emission.

The purpose of this paper is to provide an input-output life cycle assessment model to estimate the carbon footprint of US manufacturing sectors. To achieve this, the paper sets out the following objectives: develop a time series carbon footprint estimation model for US manufacturing sectors; analyze the annual and cumulative carbon footprint; analyze and identify the most carbon emitting and carbon intensive manufacturing industries in the last four decades; and analyze the supply chains of US manufacturing industries to help identify the most critical carbon emitting industries.

Initially, the economic input-output tables of US economy and carbon footprint multipliers were collected from EORA database (Lenzen et al., 2012). Then, economic input-output life cycle assessment models were developed to quantify the carbon footprint extents of the US manufacturing sectors between 1970 and 2011. The carbon footprint is assessed in metric tons of CO₂-equivalent, whereas the economic outputs were measured in million dollar economic activity.

The salient finding of this paper is that the carbon footprint stock has been increasing substantially over the last four decades. The steep growth in economic output unfortunately over-shadowed the potential benefits that were obtained from lower CO₂ intensities. Analysis of specific industry results indicate that the top five manufacturing sectors based on total carbon footprint share are “petroleum refineries,” “Animal (except poultry) slaughtering, rendering, and processing,” “Other basic organic chemical manufacturing,” “Motor vehicle parts manufacturing,” and “Iron and steel mills and ferroalloy manufacturing.”

This paper proposes a state-of-art time series input-output-based carbon footprint assessment for the US manufacturing industries considering direct (onsite) and indirect (supply chain) impacts. In addition, the paper provides carbon intensity and carbon stock variables that are assessed over time for each of the US manufacturing industries from a supply chain footprint perspective.

Extant studies have discussed numerous carbon reduction drivers, but there is a dearth of holistic review and understanding of the dynamic interrelationships between the drivers from a system perspective. Thus, this study aims to bridge that gap.

The study conducted a review using Preferred Reporting Items for Systematic Reviews and Meta-Analyses and adopted interpretive structural modelling (ISM) to analyse and prioritise the drivers.

Eighteen drivers were identified and grouped into five, namely, policy instruments, bid-related, cost and risk, education and training, and reward and penalty drivers. The ISM revealed two hierarchical levels of the drivers with only higher cost of electricity/fuel on the higher level, making it the most important driver that could influence others.

The study presents an overview of decarbonisation drivers in the literature and would be of benefit to the government and stakeholders towards achieving net zero emissions in the construction industry.

The findings of the study present drivers of carbon reduction and prioritise and categorise them for tailored interventions within the construction sector. Also, it could serve as foundational knowledge for further study in the construction process decarbonisation research area.

The purpose of this paper is to focus on tracing GHG emissions across the supply chain industries associated with the US residential, commercial and industrial building stock and provides optimized GHG reduction policy plans for sustainable development.

A two-step hierarchical approach is developed. First, Economic Input-Output-based Life Cycle Assessment (EIO-LCA) is utilized to quantify the GHG emissions associated with the US residential, commercial and industrial building stock. Second, a mixed integer linear programming (MILP) based optimization framework is developed to identify the optimal GHG emissions’ reduction (percent) for each industry across the supply chain network of the US economy.

The results indicated that “ready-mix concrete manufacturing”, “electric power generation, transmission and distribution” and “lighting fixture manufacturing” sectors were found to be the main culprits in the GHG emissions’ stock. Additionally, the majorly responsible industries in the supply chains of each building construction categories were also highlighted as the hot-spots in the supply chains with respect to the GHG emission reduction (percent) requirements.

The decision making in terms of construction-related expenses and energy use options have considerable impacts across the supply chains. Therefore, regulations and actions should be re-organized around the systematic understanding considering the principles of “circular economy” within the context of sustainable development.

Although the literature is abundant with works that address quantifying environmental impacts of building structures, environmental life cycle impact-based optimization methods are scarce. This paper successfully fills this gap by integrating EIO-LCA and MILP frameworks to identify the most pollutant industries in the supply chains of building structures.