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We conduct a multidisciplinary systematic literature review on climate neutrality in the supply chain. While carbon neutrality has gained prominence, our study argues that achieving carbon neutrality alone is not enough to address climate change effectively, as non-CO₂ greenhouse gases (GHG) are potent contributors to global warming.

We used multiple databases, including EBSCO, ProQuest, Science Direct, Emerald and Google Scholar, to identify articles related to climate neutrality in the context of non-CO₂ gases. A total of 71 articles in environmental science, climate change, energy systems, agriculture and logistics are reviewed to provide insights into the climate neutrality of supply chains.

We find that, in addition to CO₂, other GHG such as methane, nitrous oxide, ozone and fluorinated gases also significantly contribute to climate change. Our literature review identified several key pillars for achieving net-zero GHG emissions, including end-use efficiency and electrification, clean electricity supply, clean fuel supply, “GHG capture, storage and utilization,” enhanced land sinks, reduced non-CO₂ emissions and improved feed and manure management.

We contribute to the literature on climate neutrality of supply chains by emphasizing the significance of non-CO₂ GHG along with CO₂ and highlighting the need for a comprehensive approach to climate neutrality in addressing climate change. This study advances the understanding of climate neutrality of supply chains and contributes to the discourse on effective climate change mitigation strategies. It provides clear future research directions.

The aim of this study is to understand the environmental benefits and economic savings associated with adoption of circular economy in the construction sector. The research findings will support different stakeholders and decision makers to develop business models based on responsible consumption of resources and build sustainable business models.

The research uses mixed methodology wherein inventory for life cycle assessment and life cycle costing for environmental and economic impacts is based on primary data using on-site visits for qualitative and quantitative data.

Different types of land transportation infrastructures are compared for their environmental impacts. It is found that bridges have the highest environmental impacts as compared to tunnels, roads and railways. Further, the results affirm the environmental and economic benefits of adopting circular economy practices.

This is one of a kind research that compares the environmental and economic tradeoffs of adopting circular economy in different types of land transportation infrastructures.

In this chapter, the authors envision a new framework for technology-enabled local engagement. This framework would exploit web-based collaboration technology in order to create local engagement panels that represent a cross section of ordinary residents. For concreteness, the authors illustrate the framework in a proposed study called “Clearing the Air” would enable residents in the areas of Southern California that are most impacted by the logistics industry to have substantive and constructive opportunities to engage local officials on the complex economic and environmental matters related to logistics and emissions in the Southern California region. The authors propose methods to evaluate best practices in the use of collaboration technology, in particular, to learn whether the technology enables and empowers the engagement panels to participate in air quality governance at a high level and helps local officials better understand the considered opinions of residents on these important matters. If successful, this framework would integrate the policy views of individual residents alongside those of organized stakeholders, experts, and agency officials in the policy process.

The increasing accumulation of synthetic plastic waste in oceans and landfills, along with the depletion of non-renewable fossil-based resources, has sparked environmental concerns and prompted the search for environmentally friendly alternatives. Biodegradable plastics derived from lignocellulosic materials are emerging as substitutes for synthetic plastics, offering significant potential to reduce landfill stress and minimise environmental impacts. This study highlights a sustainable and cost-effective solution by utilising agricultural residues and invasive plant materials as carbon substrates for the production of biopolymers, particularly polyhydroxybutyrate (PHB), through microbiological processes. Locally sourced residual materials were preferred to reduce transportation costs and ensure accessibility. The selection of suitable residue streams was based on various criteria, including strength properties, cellulose content, low ash and lignin content, affordability, non-toxicity, biocompatibility, shelf-life, mechanical and physical properties, short maturation period, antibacterial properties and compatibility with global food security. Life cycle assessments confirm that PHB dramatically lowers CO₂ emissions compared to traditional plastics, while the growing use of lignocellulosic biomass in biopolymeric applications offers renewable and readily available resources. Governments worldwide are increasingly inclined to develop comprehensive bioeconomy policies and specialised bioplastics initiatives, driven by customer acceptability and the rising demand for environmentally friendly solutions. The implications of climate change, price volatility in fossil materials, and the imperative to reduce dependence on fossil resources further contribute to the desirability of biopolymers. The study involves fermentation, turbidity measurements, extraction and purification of PHB, and the manufacturing and testing of composite biopolymers using various physical, mechanical and chemical tests.

This study aimed to characterize the concentrations of indoor pollutants (such as carbon dioxide (CO₂), ozone (O₃), nitrogen dioxide (NO₂) and sulfur dioxide (SO₂), as well as particulate matter (PM) (PM₁, PM_2.5 and PM₁₀) in Akure, Nigeria, as well as the relationship between the parameters’ concentrations.

The evaluation, which lasted four months, used a low-cost air sensor that was positioned two meters above the ground. All sensor procedures were correctly carried out.

CO₂ (430.34 ppm), NO₂ (93.31 ppb), O₃ (19.94 ppb), SO₂ (40.87 ppb), PM₁ (29.31 µg/m3), PM_2.5 (43.56 µg/m³), PM₁₀ (50.70 µg/m³), temperature (32.4°C) and relative humidity (50.53%) were the average values obtained. The Pearson correlation depicted the relationships between the pollutants and weather factors. With the exception of April, which had significant SO₂ (18%) and low PM₁₀ (49%) contributions, NO₂ and PM₁₀ were the most common pollutants in all of the months. The mean air quality index (AQI) for NO₂ indicated that the AQI was “moderate” (51–100). In contrast to SO₂, whose AQI ranged from “moderate” to “very unhealthy,” O₃'s AQI ranged from “good” (50) to “unhealthy” (151–200). Since PM₁, PM_2.5 and PM₁₀ made up the majority of PC1’s contribution, both PM_2.5 and PM₁₀ were deemed “hazardous.”

The practical implication of indoor air pollution is long-term health effects, including heart disease, lung cancer and respiratory diseases such as emphysema. Indoor air pollution can also cause long-term damage to people’s nerves, brain, kidneys, liver and other organs.

Lack of literature in terms of indoor air quality (IAQ) in Akure, Ondo State. With this work, the information obtained will assist all stakeholders in policy formulation and implementation. Again, the low-cost sensor used is new to this part of the world.

This study aims to quantify and compare the environmental impacts of Marble-stone and Kota-stone flooring options widely used for buildings in India. The study discusses the possibility of carbon sequestration through Bamboo cultivation in India.

The study has followed a standard life cycle assessment (LCA) framework based on ISO 14040 guidelines. Three distinct phases have been compared on midpoint and endpoint assessment categories – raw material, polishing and disposal. Primary data has been collected from the construction site in India, and secondary data has been collected from the Ecoinvent 3.0 database. Previous studies have been referred to discuss and calculate the area of bamboo cultivation required to sequestrate the generated carbon from the flooring.

The study has found that endpoint category damage to resources, and midpoint categories of climate change, metal depletion and agricultural land use are highly impacted in building floorings. The study has also found that the Marble-stone floor generates higher environmental impacts than the Kota-stone floor in most of the midpoint and endpoint impact categories. This difference is significant in the raw material phase due to the different compositions of stones. The study also found that Bamboo has excellent potential to act as a carbon sink and mitigate the generated carbon.

This study excludes human labour, cutting and distribution of floor tiles made of Marble-stone and Kota-stone. The researcher can use the study to evaluate, compare and benchmark the various building flooring options from the environmental perspective. The study aids to the body of knowledge available on the various building flooring options by presenting the LCA or the environmental impacts generated by two flooring options. It is expected that the architects and builders can use these results to develop carbon-neutral buildings. This study provides a methodology for governments, constructors, builders and individuals to evaluate, compare and benchmark the various construction materials from the environmental perspective by computing the environmental impacts throughout the life cycle of the materials.

This study compares two widely used building flooring options using the LCA methodology and evaluates the potential of bamboo cultivation near the buildings for carbon sinks. The study is unique because it shows the environmental impacts of two flooring options and the carbon sequestration method to mitigate/absorb the generated environmental impacts in or around the building itself through bamboo cultivation. This study may set the foundation for carbon-neutral buildings.

This paper examines how manufacturing firms impacted by the nitrogen oxides (NO_x) Budget Trading Program (NBP) strategically managed working capital to release funds for increased costs and mitigate the negative impact on firm performance.

The study uses a panel data set including 11,302 manufacturing firm-year observations listed on the US exchanges during the period 2000–2008. The authors use Tobin's Q to proxy for firm performance, and cash holding, cash conversion cycle (CCC), days sales outstanding (DSO), days sales inventory (DSI) and days payable outstanding (DPO) for working capital management (WCM). The empirical analysis is conducted using both ordinary least squares (OLS) and propensity score matching (PSM) regressions.

The authors find that firms respond to the higher utility costs imposed by the NBP by decreasing CCC, DSO and DSI. This active WCM response partially mitigated the impact of increased compliance costs on performance for firms affected by the NBP. Results are robust in PSM regressions.

Climate change is a global issue that has attracted increasing attention in recent years. This study shows how firms can adjust short-term financing strategies to address the costs of compliance with climate change regulation.

The paper contributes to the emerging literature on corporate finance and climate policy actions. The authors use the unique experimental setting of the NBP to examine the regulatory impact on corporate financial management. The authors demonstrate how firms used active WCM to mitigate the negative performance impact of regulatory compliance with the NBP, providing novel insight on the implication of compliance with climate change legislation.

Indoor air quality monitoring is extremely important in urban, industrial areas. Considering the devastating effect of declining quality of air in major part of the countries like India and China, it is highly recommended to monitor the quality of air which can help people with respiratory diseases, children and elderly people to take necessary precautions and stay safe at their homes. The purpose of this study is to detect air quality and perform predictions which could be part of smart home automation with the use of newer technology.

This study proposes an Internet-of-Things (IoT)-based air quality measurement, warning and prediction system for ambient assisted living. The proposed ambient assisted living system consists of low-cost air quality sensors and ESP32 controller with new generation embedded system architecture. It can detect Indoor Air Quality parameters like CO, PM2.5, NO2, O3, NH3, temperature, pressure, humidity, etc. The low cost sensor data are calibrated using machine learning techniques for performance improvement. The system has a novel prediction model, multiheaded convolutional neural networks-gated recurrent unit which can detect next hour pollution concentration. The model uses a transfer learning (TL) approach for prediction when the system is new and less data available for prediction. Any neighboring site data can be used to transfer knowledge for early predictions for the new system. It can have a mobile-based application which can send warning notifications to users if the Indoor Air Quality parameters exceed the specified threshold values. This is all required to take necessary measures against bad air quality.

The IoT-based system has implemented the TL framework, and the results of this study showed that the system works efficiently with performance improvement of 55.42% in RMSE scores for prediction at new target system with insufficient data.

This study demonstrates the implementation of an IoT system which uses low-cost sensors and deep learning model for predicting pollution concentration. The system is tackling the issues of the low-cost sensors for better performance. The novel approach of pretrained models and TL work very well at the new system having data insufficiency issues. This study contributes significantly with the usage of low-cost sensors, open-source advanced technology and performance improvement in prediction ability at new systems. Experimental results and findings are disclosed in this study. This will help install multiple new cost-effective monitoring stations in smart city for pollution forecasting.

One of the characteristics of our age is urbanization and the increase in the population of cities and the development of small and large cities. In fact, the sustainable development of a city, which in its general sense means all-round growth and expansion without contradiction, with two main goals: (1) improving the quality of life in terms of the capacity of the environment and (2) responding to the needs of the present generation without restrictions for the future ones. But according to the fifth wave theory, entering the first edge of the future from 2020 to 2030, with the development of urbanization, we are witnessing instability in recent decades. Depletion of forests, thinning of the ozone layer, accumulation of greenhouse gases, noise pollution, threatening air pollution in cities are among these indicators in urban areas. Other important indicators also show the social and economic instability of the world community. The increase in poverty, economic growth without job creation, increase the gap between developed and developing countries, social disintegration, and the sense of citizenship of the residents of local communities and the class gap are among these indicators. Therefore, since 1970, with the advancement of technologies, today the realization of urban development depends on attention to the factors of innovation, digitalization, and intelligentization. In the concept of development, aspects, domains, and scales become important. This improvement of the current situation will be realized when it brings all-round intelligence without conflict and contradiction in meeting the tangible and intangible needs of this society. Growth in material and spiritual aspects, from local to global scales and in different economic, cultural, educational, social, technological, and even political fields. Therefore, paying attention to the quality makes the concept of development more effective. These qualities are aligned with the concept of sustainability. In this chapter, U-city, smart sustainable mobility through 5th Wave, i-Sustainability Plus, and doost cultural theory (DCT) will be probed as a new concept of urban living for blue-green smart city and mobility. In addition, challenges made some countries not to prefer to utilize smartness and ubiquitous idea as a tool for their problems, will be explored.

The study aims to develop a multilayer high-effective ensemble of ensembles predictive model (stacking ensemble) using several hyperparameter optimized ensemble machine learning (ML) methods (bagging and boosting ensembles) trained with high-volume data points retrieved from Internet of Things (IoT) emission sensors, time-corresponding meteorology and traffic data.

For a start, the study experimented big data hypothesis theory by developing sample ensemble predictive models on different data sample sizes and compared their results. Second, it developed a standalone model and several bagging and boosting ensemble models and compared their results. Finally, it used the best performing bagging and boosting predictive models as input estimators to develop a novel multilayer high-effective stacking ensemble predictive model.

Results proved data size to be one of the main determinants to ensemble ML predictive power. Second, it proved that, as compared to using a single algorithm, the cumulative result from ensemble ML algorithms is usually always better in terms of predicted accuracy. Finally, it proved stacking ensemble to be a better model for predicting PM_2.5 concentration level than bagging and boosting ensemble models.

A limitation of this study is the trade-off between performance of this novel model and the computational time required to train it. Whether this gap can be closed remains an open research question. As a result, future research should attempt to close this gap. Also, future studies can integrate this novel model to a personal air quality messaging system to inform public of pollution levels and improve public access to air quality forecast.

The outcome of this study will aid the public to proactively identify highly polluted areas thus potentially reducing pollution-associated/ triggered COVID-19 (and other lung diseases) deaths/ complications/ transmission by encouraging avoidance behavior and support informed decision to lock down by government bodies when integrated into an air pollution monitoring system

This study fills a gap in literature by providing a justification for selecting appropriate ensemble ML algorithms for PM_2.5 concentration level predictive modeling. Second, it contributes to the big data hypothesis theory, which suggests that data size is one of the most important factors of ML predictive capability. Third, it supports the premise that when using ensemble ML algorithms, the cumulative output is usually always better in terms of predicted accuracy than using a single algorithm. Finally developing a novel multilayer high-performant hyperparameter optimized ensemble of ensembles predictive model that can accurately predict PM_2.5 concentration levels with improved model interpretability and enhanced generalizability, as well as the provision of a novel databank of historic pollution data from IoT emission sensors that can be purchased for research, consultancy and policymaking.