• Energy Research
• 2021-2025close
• 9. Industry and infrastructure close
• US close
• RU close
• IT close
• DE close
• EU close

Purpose This study aims to consider environmental sustainability, a global challenge under the preview of sustainable development goals, highlighting the significance of knowledge economy in attaining sustainable aggregate demand behavior globally. For this purpose, 155 countries that have data available from 1995 to 2021 were selected. The purpose of selecting these countries is to test the global responsibility of the knowledge economy to attain environmental sustainability. Design/methodology/approach Results are estimated with the help of panel quantile regression. The empirical existence of aggregate demand-based environmental Kuznets curve (EKC) was tested using non-linear tests. Moreover, principal component analysis has been incorporated to construct the knowledge economy index. Findings U-shaped aggregate demand-based EKC at global level is validated. However, environmental deterioration increases with an additional escalation after US$497.945m in aggregate demand. As a determinant, the knowledge economy is reducing CO2 emissions. The knowledge economy has played a significant role in global responsibility, shifting the EKC downward and extending the CO2 reduction phase for every selected country. Further, urbanization, energy intensity, financial development and trade openness significantly deteriorate the environmental quality. Originality/value This study contains the empirical existence of aggregate demand-based EKC. The role of the knowledge economy is examined through an index which is calculated by using four pillars of the knowledge economy (technology, innovations, education and institutions). This study is based on a combined panel of all the countries for which the data was available.

This dataset holds information on data and methods for the Melvin et al., 2016 publication entitled Climate change damages to Alaska public infrastructure and the economics of proactive adaptation. The abstract for this paper is as follows: Climate change in the circumpolar region is causing dramatic environmental change that is increasing the vulnerability of infrastructure. We quantified the economic impacts of climate change on Alaska public infrastructure under relatively high and low climate forcing scenarios [representative concentration pathway 8.5 (RCP8.5) and RCP4.5] using an infrastructure model modified to account for unique climate impacts at northern latitudes, including near-surface permafrost thaw. Additionally, we evaluated how proactive adaptation influenced economic impacts on select infrastructure types and developed first-order estimates of potential land losses associated with coastal erosion and lengthening of the coastal ice-free season for 12 communities. Cumulative estimated expenses from climate-related damage to infrastructure without adaptation measures (hereafter damages) from 2015 to 2099 totaled $5.5 billion (2015 dollars, 3% discount) for RCP8.5 and $4.2 billion for RCP4.5, suggesting that reducing greenhouse gas emissions could lessen damages by $1.3 billion this century. The distribution of damages varied across the state, with the largest damages projected for the interior and southcentral Alaska. The largest source of damages was road flooding caused by increased precipitation followed by damages to buildings associated with near-surface permafrost thaw. Smaller damages were observed for airports, railroads, and pipelines. Proactive adaptation reduced total projected cumulative expenditures to $2.9 billion for RCP8.5 and $2.3 billion for RCP4.5. For road flooding, adaptation provided an annual savings of 80–100% across four study eras. For nearly all infrastructure types and time periods evaluated, damages and adaptation costs were larger for RCP8.5 than RCP4.5. Estimated coastal erosion losses were also larger for RCP8.5.

The proposed special issue (SI) has invited submissions related to renewable energy, energy storage, power converters and electric drive systems for electrified transportation and smart grid applications [...]

Abstract Mitigating the effects of environmental deterioration requires a focus on not just CO2 emissions from energy consumption, but also environmental pollution from industry sectors. To reach this goal, recent studies have extended ecological footprint (EF) analysis to identify the ecological drivers of various key industry sectors. The role of the phosphorus (P) industry on the EF within the environmental Kuznets curve (EKC) framework for China is the emphasis of this study. Autoregressive distributive lag (ARDL) as well as the impulse response function and robustness analysis were used to consider a time from 1985 to 2018. The study verifies the EKC hypothesis for China in both the long and the short run, and indispensable determinants are proposed to be included to assure the model’s fitness and robustness when conducting EF analysis of industry sectors. Energy consumption–based carbon emissions have been verified as the dominant contributor to EF, but P use and urbanization have a significant lagged positive influence on EF in the short run. P exports, in particular, have been highlighted as a critical driver of the EF of China’s P industry. The conducted frequency domain causality test reinforced the above findings and demonstrated bidirectional causality at different frequencies. This work suggests that formulating plausible P export policies to alleviate the conflict between the output of China’s P industry and the environmental sustainability of this industry are necessary. In this context, “multidisciplinary, multidimensional, and practical solutions” are most desirable for sustainable P management.

Sustainability aspects and their verification are becoming indispensable for companies in the textile industry from both an economic and a legal perspective. The reason for this is that there is a large number of different certificates, specifications, and labels, such as Global Organic Textile Standard, Fairtrade, or OekoTex, as well as legislation, such as the German Act on Corporate Due Diligence Obligations in Supply Chains issued in 2021. Hence, the requirements for keeping the proof, e.g. for the batch-accurate world-wide tracing of organic cotton for clothing, or for the necessary transparency to determine the carbon footprint or the recycling percentage, are becoming more and more associated with considerable effort, especially for small and medium-sized enterprises (SMEs). Depending on the certificate or specification, SMEs need not only to determine their own sustainability information (gate-to-gate), but also that of the upstream stages of the value chain (cradle-to-gate). The multi-stage value chains of the SME-dominated textile industry, together with the vast and fast-changing variety of materials and products, lead to high complexity in processes and communication. In addition, when confronted with batch-related sustainability criteria and a variety of sustainability and labelling requests from different customers, SMEs have to spend an increasing amount of time and effort on the reliable provision and communication of the respective information. The paper describes the challenges and existing approaches, e.g. the use of blockchain technology, associated with the provision of cradle-to-gate sustainability information in textile SMEs and proposes a holistic framework enabling SMEs along the value chain to configure and implement an infrastructure for efficient, fully digital cloud-ready workflow, based on process models and textile product master trees, in order to address these challenges.

Eco-innovation has gained considerable attention in the academic and corporate world due to its potential in mitigating a firm’s sustainability issues. Most previous studies focused on the reactive approaches of eco-innovation using primary data. This study mitigates the existing research gap by highlighting proactive eco-innovation and using a secondary panel dataset. The prime objective of this study is to investigate the relationship between proactive eco-innovation and firms’ financial performance. Hence, the study will introduce the proactive eco-innovation index with the help of secondary panel data. In addition to that, the paper will also explore how proactive eco-innovation relates to circular economy. The theory of Resource-Based View (RBV) was used to explain the relationship among the variables. This study was conducted on 31 Malaysian public listed energy companies from 2015 to 2019. A proactive eco-innovation index was inferred by adapting three dimensions of eco-innovation (product, process, and technology) which is applicable for the energy sector. By applying random-effects GLS regression equation modeling, it was found that proactive eco-innovation (product eco-innovation, process eco-innovation, and technology eco-innovation) has a direct effect on firm financial performance. Furthermore, product and process eco-innovation is directly related to a circular economy through a sustainable product development process. The findings suggest that policymakers in the firm should proactively adopt eco-innovative practices. It will positively affect the circular economy as it will be cost-effective and help to reduce potential industrial pollution in the environment.

Abstract In modern developed economies, one of the primary objectives is to manage the transition from polluting to cleaner technologies as efficiently as possible. By now, in the current empirical literature, one can identify technological spillovers from environmental innovations as a major driver of this process. Specific energy policy aspects connected with industry behaviour have yet to be explored. The aim of this paper is to investigate energy efficiency via environmental innovation and the resulting degree of resilience and adaptation of both developed and developing countries. The work applies the non-parametric DEA (Data Envelopment Analysis) framework and Tobit analysis. For this scope, it is built a panel dataset made of some 5000 observations based on energy policy and sustainable development variables for 136 OECD and non-OECD countries. The results show that knowledge spillovers from environmental innovations reduce inefficiency and therefore strengthen the resilience of economies that decide and manage to invest adequately in the transition to more sustainable technologies. Besides, OECD countries improve their energy efficiency scores over time, whilst non-OECD countries do not. This implies that sustainable technologies transition is made more efficient by environmental innovation but the process is fostered by disposing of a resilient economic system – hence, vulnerability can affect the transition. These hypotheses lead to important economic, social and environmental implications for energy policy modelling.

The construction industry is considered as one of the least productive, highest energy consuming, and least digitized industries. The Lean Management (LM) philosophy became a significant way for eliminating non-value-added activities and wastes during a building’s lifecycle. However, studies have shown that philosophies are not efficient by themselves to solve the issues of the construction industry. They need to be supported with the appropriate technologies and tools. Therefore, the integrated use of Building Information Modelling (BIM) with LM or Value Engineering (VE) were proposed in the literature. Nonetheless, it was also seen that BIM can provide more insights and improvements when BIM is integrated with data analysis tools to analyze BIM data. In the literature, the synergies between these concepts are generally addressed pairwise, and there is no comprehensive framework which identifies their relationships. Therefore, this study aims to develop a maturity framework that facilitates the adoption of LM, VE, BIM, and Big Data Analytic (BDA) concepts to address long-standing productivity and digitalization issues in the Architecture, Engineering, and Construction (AEC) industry. Design Science Research (DSR) methodology and its three-cycle view (relevance, rigor, and design cycle) were applied to build the proposed maturity framework. Two interviews were performed to identify and observe research problem in relevance cycle. In the rigor cycle, a comprehensive literature review was performed to create a base for the development of the maturity framework. In addition to the developed base of the framework, lean processes were added to this cycle. In the design cycle, the developed framework was evaluated and validated by five experts through face-to-face interviews. The importance of employer’s requirements to adopt the proposed methodologies, the negative impact of change orders, the importance of pre-construction phases to facilitate value creation and waste elimination, and the usage of common data environment with BIM were identified as the prominent application and adaptation issues.

At the present stage of the transition to sustainable development, when the share of a green economy in technologically advanced countries reaches one third of GDP, overcoming the structural crisis of the Russia is inseparable from reducing the technological gap and structural imbalances in the economy. These imbalances alienate Russia from ecologically and technologically advanced countries. Therefore, it is highly relevant to develop a methodology for assessing the structural convergence of the economy in the context of the transition to sustainable development, accompanied in developed countries by a new type of structural shift caused by the expansion of convergent technologies, the emergence of new principles of sectoral genesis and the formation of a green economy. The deepening of the divergent nature of Russian economy development was the result of a series of negative structural shifts that consolidated the dominance of extractive and resource-intensive industries, which threaten the final loss of technological identity, consolidation of the recessive trend and a lag in the transition to sustainable development. Therefore, today it is extremely important to formulate a methodology for assessing the structural convergence of Russian economy with technological and ecologically advanced countries, taking into account both quantitative and qualitative aspects.