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Now more than ever, climate action requires both private and public investment in building a sustainable future for all. COP26 affirmed the importance of collective action at all scales coupled with supporting public policy to limit global warming to a 1.5-degree trajectory. This chapter outlines the process and building blocks that culminated in the launch of the Drawdown Georgia Business Compact, whose mission is to leverage the collective impact of Georgia’s business community to achieve net zero carbon emissions in the state by 2050. In bringing together companies across diverse industries, the Business Compact creates a community of practice where cross-sector collaboration accelerates Georgia’s path to actualizing COP26’s decarbonization vision while also considering ‘beyond carbon’ issues such as the economy, equity, public health, and the environment. This is a regional and voluntary approach to Sustainable Development Goal (SDG) 17 (Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development), which recognizes multi-stakeholder partnerships as important vehicles to achieve SDGs.

Classification of remote sensing images (RSI) is a challenging task in computer vision. Recently, researchers have proposed a variety of creative methods for automatic recognition of RSI, and feature fusion is a research hotspot for its great potential to boost performance. However, RSI has a unique imaging condition and cluttered scenes with complicated backgrounds. This larger difference from nature images has made the previous feature fusion methods present insignificant performance improvements.

This work proposed a two-convolutional neural network (CNN) fusion method named main and branch CNN fusion network (MBC-Net) as an improved solution for classifying RSI. In detail, the MBC-Net employs an EfficientNet-B3 as its main CNN stream and an EfficientNet-B0 as a branch, named MC-B3 and BC-B0, respectively. In particular, MBC-Net includes a long-range derivation (LRD) module, which is specially designed to learn the dependence of different features. Meanwhile, MBC-Net also uses some unique ideas to tackle the problems coming from the two-CNN fusion and the inherent nature of RSI.

Extensive experiments on three RSI sets prove that MBC-Net outperforms the other 38 state-of-the-art (STOA) methods published from 2020 to 2023, with a noticeable increase in overall accuracy (OA) values. MBC-Net not only presents a 0.7% increased OA value on the most confusing NWPU set but also has 62% fewer parameters compared to the leading approach that ranks first in the literature.

MBC-Net is a more effective and efficient feature fusion approach compared to other STOA methods in the literature. Given the visualizations of grad class activation mapping (Grad-CAM), it reveals that MBC-Net can learn the long-range dependence of features that a single CNN cannot. Based on the tendency stochastic neighbor embedding (t-SNE) results, it demonstrates that the feature representation of MBC-Net is more effective than other methods. In addition, the ablation tests indicate that MBC-Net is effective and efficient for fusing features from two CNNs.

This study aims to propose priority solutions for mitigating the impact of the Covid-19 pandemic through the Social Safety Net (SSN) based on the Islamic objectives.

The analytic network process method is used in this study. Focus group discussions and in-depth interviews with relevant stakeholders were used to collect data and supplemented by a literature review to explore comprehensive information.

The findings indicate different opinions among experts, including regulators, practitioners, associations and academics regarding the most important priority solutions to the impact of Covid-19. However, experts agree that the highest priority solution is the SSN program in the lineage sector, specifically the distribution of the Family Hope Program. A program in the field of protecting the mind is the second priority solution. The program in the field of soul and wealth is the third priority solution.

This study contributes to the development of government policy to deal with the socioeconomic impact of Covid-19 based on the Islamic objectives by mapping the SSN through the five elements of protection, namely, faith, soul, mind, lineage and wealth protection based on priority solution.

The versatility of customer relationship management (CRM) systems has kept these technologies popular over the years. These solutions have been integrated into organizations of all sizes, from large corporations to small- and medium-sized enterprises. Similarly, CRM systems have also found applications in all types of industries and business sectors. All this has been the driving force behind the proliferation of CRM solutions around the world. In this chapter, the author not only reflects on the impact and democratization of CRM systems on business management and marketing strategies but also explores how these technologies can determine the company's income. In particular, the author presents an experiment that analyzes the extent to which the volume of annual investment in CRM solutions can be used to predict annual net income in a sample of companies. Using time series analysis and applying the autoregressive integrated moving average modeling technique, the researcher examines a sample of 10 companies from different industries, and countries, over a 20-year period. The results show the efficiency of the predictive models developed in nine of the 10 companies analyzed. The findings of this study allow us to conclude that there seems to be an association between the investments made in CRM solutions and the income of the companies that invest in these technologies.

This paper aims to present two different methods to speed up a test used in the sanitary ware industry that requires to count the number of granules that remains in the commodity after flushing. The test requires that 2,500 granules are added to the lavatory and less than 125 remain.

The problem is approached using two deep learning computer vision (CV) models. The first model is a Vision Transformers (ViT) classification approach and the second one is a U-Net paired with a connected components algorithm. Both models are trained and evaluated using a proprietary data set of 3,518 labeled images, and performance is compared.

It was found that both algorithms are able to produce competitive solutions. The U-Net algorithm achieves accuracy levels above 94% and the ViT model reach accuracy levels above 97%. At this time, the U-Net algorithm is being piloted and the ViT pilot is at the planning stage.

To the best of the authors’ knowledge, this is the first approach using CV to solve the granules problem applying ViT. In addition, this work updates the U-Net-Connected components algorithm and compares the results of both algorithms.

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.

This chapter discusses the global challenge to reduce greenhouse gas (GHGs) emissions to net zero by 2050. It explains what net zero means and how it is calculated, together with some of the debate around the suitability of the target to maintain global warming levels within ‘acceptable’ boundaries. The chapter then presents some of the opportunities and challenges that transitioning towards net zero will pose to countries and their inhabitants, in terms of changes to policies, products, processes and behaviours that will be required to attain the target. It then discusses the need for a strategy to achieve net zero across different sectors of society and provides a few suggestions of tools and concepts that could be adopted to support the changes necessary, such as planning for change, the Sustainable Development Goals (SDGs), integrated reporting and the circular economy. The chapter concludes with a reflection on the need for the net zero target and how it is our collective responsibility to support the challenging transition to net zero for the benefit of all.

This study aims to report business preferences for achieving net-zero power production emissions in Saskatchewan, Canada as well as business perceptions of the most preferable power production sources, barriers to change and suggestions for improvement. Mixed methods included focus groups and a survey with experimental design. This research demonstrates that this method of advancing academic and business knowledge systems can engender a paradigmatic shift to decarbonization.

The study is a mixed-methods study using five focus groups and a survey which included a 15-min information video providing more information on power production sources (small modular reactors and biomass). Participants requested more information on these topics in the initial three focus groups.

There is a significant gap in Canadian Government targets for net-zero emissions by 2050 and businesses’ plans. Communications, knowledge and capacity gaps identified include lack of regulatory requirements, institutional barriers (including a capacity charge in the event a business chooses to self-generate with a cleaner source) and multi-level governance dissonance. More cooperation between provincial governments and the federal government was identified by participants as a requirement for achieving targets. Providing information to survey respondents increased support for clean and renewable sources, but gender and knowledge are still important characteristics contributing to support for different power production sources. Scientists and teachers were the most trusted sources of information. Power generated from small modular nuclear reactors was identified as the primary future source of power production followed by solar, wind and natural gas. Research results also confirmed the high level of support for hydropower generated in Saskatchewan versus import from Manitoba based on high values of energy solidarity and security within the province.

This study is original, as it concerns upstream system power production portfolios and not failed projects; the mixed-method research design including a focus group and an experimental survey is novel. This research partially addresses a gap in knowledge surrounding which knowledge systems advance paradigmatic shifts and how and whether involving business people in upstream power production decisions can inform decarbonization.

As the number of web applications is increasing day by day web mining acts as an important tool to extract useful information from weblogs and analyse them according to the attributes and predict the usage of a website. The main aim of this paper is to inspect how process mining can be used to predict the web usability of hotel booking sites based on the number of users on each page, and the time of stay of each user. Through this paper, the authors analyse the web usability of a website through process mining by finding the web usability metrics. This work proposes an approach to finding the usage of a website using the attributes available in the weblog which predicts the actual footfall on a website.

PROM (Process Mining tool) is used for the analysis of the event log of a hotel booking site. In this work, authors have used a case study to apply the PROM (process mining tool) to pre-process the event log dataset for analysis to discover better-structured process maps than without pre-processing.

This article first provided an overview of process mining, then focused on web mining and later discussed process mining techniques. It also described different target languages: system nets (i.e. Petri nets with an initial and a final state), inductive miner and heuristic miner, graphs showing the change in behaviour of the dataset and predicting the outcome, that is the webpage having the maximum number of hits.

In this work, a case study has been used to apply the PROM (process mining tool) to pre-process the event log dataset for analysis to discover better-structured process maps than without pre-processing.

The aim of this paper is to discuss a selection of policy strategies, regional initiatives and market approaches to uncover the realities of twenty-first-century building energy performance. A position that market-based approaches, human influence and policy interventions are part of an ecosystem of building energy performance is presented.

An exploratory search of secondary sources spanning the last three decades was conducted. Both peer-reviewed and grey literature were included to capture a broader understanding of the discourse in literature. Research questions guided the literature search, and a data extraction tool was designed to categorise the literature. The primary limitation of this study is that only a few applications could be discussed in a condensed format.

Several challenges about the current status quo of building energy performance were identified and summarised as follows. (1) Inconsistencies in measurement and verification protocols, (2) Impacts of market approaches, (3) National policy priorities that are at variance with regional targets and (4) Ambiguous reporting on environmental impacts of energy efficiency (EE) technologies.

The practical implications of the findings in this paper for practice and research are that as part of the building energy performance ecosystem, national responses through government interventions must become adaptive to keep up with the fast-paced energy sector and social trends. Simultaneously, before market-based approaches overcome the messiness of socio-economic dynamics, institutional conditions and cultural nuances, they ought to transparently address environmental impacts and the infringement of several SDGs before they can become viable solutions to building energy performance.

This paper presents building energy performance as an ecosystem comprising human influence, market-based approaches and policy interventions which form interdependent parts of the whole. However, evidence in the literature shows that these aspects are usually investigated separately. By presenting them as an ecosystem, this paper contributes to the discourse by advocating the need to re-align building energy performance to socio-economic-political dynamics and contextually viable solutions.