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This paper presents research performed on set-based multi-criteria optimization for the preliminary design of slab frame bridges. As a result of immense CO2 emissions emerging from concrete production, there is a need for optimization methods decreasing the volume of concrete without affecting the function of the structure. Previous research suggests a general correlation between CO2 footprint- and cost reduction, due to minimized material use. However, the aspect of buildability may conflict with lessened material, as optimized designs might simultaneously be less buildable. This research aimed to develop an optimization method with respect to the investment cost and environmental impact while also considering buildability cost aspects. The optimization algorithm shows the possibilities of reducing the environmental impact by up to 13.7% for a slightly increased cost of 2.3%. Thus, by implementing optimization procedures in the early stages of the planning process the holistic cost effects related to green solutions can be presented, favoring the choice of sustainable designs amongst clients during tendering procedures. [ABSTRACT FROM AUTHOR]

Growing awareness of the need to enhance environmental performance resonates across various industrial sectors. However, efforts toward sustainability remain inadequate, especially given the pressing issue of Climate Change. To address this, this paper introduces a novel preliminary approach grounded in Lean Thinking principles, featuring a structured three-step process enhanced with visual tools. Step one involves pinpointing the green sustainability measures and their deployment locations, facilitating the identification of urgent actions. Step two delves into identifying potential barriers that could hinder measure adoption, resulting in a refined set of implementable actions for greater improvements. Finally, in the third step, we select and prioritize the most viable measures, accounting for case-specific factors like costs and their impact on environmental performance. This study culminates in a real-world application within a luxury plywood production company, illustrating the framework's ability to align environmental objectives with economic success by avoiding unprofitable choices. [ABSTRACT FROM AUTHOR]

With the growing global energy demand and limited production of conventional crude oil, the extraction of unconventional heavy and extra-heavy oil is receiving unprecedented attention. China is the fourth largest producer of heavy oil. High viscosity and complex formation environments increase the difficulty of exploiting heavy oil. To alleviate the economic cost and environmental pollution pressure brought by heavy oil production, China has achieved significant advancement in the innovation of heavy oil recovery, including accelerating the transformation of thermal technology to non-thermal technology and the use of new energy technology to achieve the goal of low-carbon economy and green environmental protection. Based on the background of heavy oil in China, this paper recharacterizes the high-viscosity mechanism of heavy oil. From different aspects, including basic principles, main characteristics, applicability, limitations, and challenges, this paper comprehensively reviews existing heavy oil recovery technologies, including thermal recovery, in-situ upgrading, cold recovery, and new energy technologies. Currently, in China, the main target of heavy oil recovery is achieving the "carbon peaking and carbon neutrality" goals, which can be described as low energy consumption, low emission, and low pollution. To facilitate the low-carbon transition in the extraction of heavy/extra-heavy oil and attain environmental sustainability goals, it is essential to increase the proportion of non-thermal factors in thermal recovery technologies, develop efficient in-situ catalysts, adopt clean cold extraction techniques, and advance the development of new energy technologies. [ABSTRACT FROM AUTHOR]

Exergy-based methods consist of at least an exergetic analysis, which can be combined with an economic analysis and a life cycle assessment in order to evaluate and improve energy-intensive processes. The author coined the term exergoeconomics in 1984 to replace the term thermoeconomics when exergy costing is used in the combination of an exergetic analysis with a cost analysis and to emphasize the role of exergy in the efforts to reduce the product cost. He also developed a general exergoeconomic methodology based on exergy-based variables and on appropriate definitions of the "product" and "fuel" for each component of an energy conversion system. These definitions and the application of exergoeconomics were generalized by A. Lazzaretto and the author in 2006 in an approach based on specific costs (SPECO approach). L. Meyer and the author coined the term exergoenvironmental analysis in 2009 for the combination of an exergy analysis with a life-cycle analysis when exergy is used to assign environmental impacts to streams. An exergoenvironmental analysis uses the methodological background of exergoeconomic methods. Starting in 2002, to reduce the limitations of the conventional exergy-based methods, and to facilitate system optimization, the author, and later in cooperation with T. Morosuk and co-workers at TU Berlin, developed the advanced exergy-based methods, which are based on the notion that the inefficiencies (exergy destruction and exergy loss), the costs, and the environmental impacts can be split into avoidable/unavoidable and endogenous/exogenous parts, to improve understanding of the formation process of inefficiencies, costs and environmental impacts within an energy conversion system. As often happens, after their introduction, all the above terms and methods have been misused and misinterpreted by some other authors. This paper briefly reviews past contributions by the author and some other exergy practitioners and discusses future developments. It concludes, that the advanced exergy-based methods need further development to be generalized, and integrated and to reduce their subjectivity in addition to the efforts and time required for their application. Development of appropriate software and shortcut methods will facilitate their use by researchers and engineers in industry and the application of exergy-based methods in energy-intensive industrial processes for multiobjective optimization purposes. • The most promising exergy-based methods are briefly reviewed. • Expected future developments are discussed. • More industrial applications are needed. • Misinterpretations and misuses of the methods limit their development. • The methods can be used to fairly apportion pollutants among countries. [ABSTRACT FROM AUTHOR]

Over recent decades, China's economy has continued to develop steadily and rapidly, contributing to a large environmental cost. Therefore, many researchers propose various methods to evaluate environmental efficiency to deal with these serious issues accurately. This paper is the first to evaluate environmental efficiency using a model featuring not only a parallel structure with three component processes (primary, secondary, and tertiary industries) in the first stage but also a sewage treatment process in the second stage. Simultaneously, we propose radial but also nonradial versions of the Environmental Multiple Variable Return to Scale (EMVRS) model. These two models both consider shared inputs as well as shared outputs, and the proportion of the shared inputs and shared outputs utilized in each component process is assumed to be unknown. Additionally, we further analyze the factors influencing environmental efficiency by utilizing the Tobit model. The results when EMVRS is applied to Chinese provinces can be divided into 3 parts: (1) Most provinces do not purify wastewater appropriately contributing to the bad environmental efficiency. (2) The efficiency values of the three industry types are similar or identical to each other in most provinces. (3) The population of people employed negatively impacts environmental efficiency. [ABSTRACT FROM AUTHOR]

The environmental crisis induced by global warming has generated an increasingly heightened recognition of the imperative to embark on a journey toward sustainability. In this paper, we focus on the price correlations between two potential emission mitigation instruments: green bond (GB) and the carbon trading market. The Cross-Quantilogram (CQ) method is employed to identify these relationships within Chinese markets spanning from July 16, 2021, to March 31, 2023. Our findings reveal a diverse array of dependence structures, which are most pronounced at the first lag and tend to diminish with subsequent lags. Further analysis through the Partial Cross-Quantilogram (PCQ) approach reveals that the dependence structure is reshaped by both micro-market volatility and climate policy uncertainty, but the latter exerts influence at more extended lags. Likewise, these correlations are subject to temporal shifts, with coefficients displaying heightened magnitude and increased volatility in extreme scenarios compared to normal states. Therefore, investors and industries seeking to participate in the carbon trading market should closely monitor the performance of green bonds, particularly during tumultuous periods. • We apply the Cross-Quantilogram method to pinpoint correlations between green bond market and carbon markets. • The analysis exposes varied dependence patterns, most evident at the lag 1. • The dependence structure is reshaped by both micro-market volatility and climate policy uncertainty. • Correlations fluctuate over time, with marked intensification in extreme conditions. [ABSTRACT FROM AUTHOR]

• • Cost efficiency analysis of vehicle technologies for semi-flexible transit. • • Total system cost estimated includes operator, user, and environmental costs. • • Diesel and battery-electric vehicle technology evaluated for three vehicle sizes. • • Optimal values of service headway and slack time are identified. • • Analysed sensitivity to charging speed, demand, and battery capacity. In low-demand areas, semi-flexible transit system (SFT) operated by battery electric vehicles (BEVs) can reduce operational costs and achieve zero emissions, allowing SFT to be used more widely, and transit agencies to benefit more significantly. This paper is aimed at analyzing the effect of the additional requirements of BEVs on the cost efficiency of SFT services while considering different headways and slack time to accommodate route-deviation. Analytical models are used for detailed estimation of the total cost, including operator, user, and environmental costs, allowing a comparison with internal combustion engine (ICEV) vehicle technology, and three vehicle sizes: minivans, standard vans, and minibuses. Study results can be used to evaluate budget requirements to upgrade an existing ICEV based standard bus service along an underperforming low demand route to a BEV based SFT service. The application of the proposed methodology is demonstrated for a low-demand bus route in Regina, Canada. [ABSTRACT FROM AUTHOR]

This study examined the relationship between government environmental governance (GEG) and regional green development. The findings suggest that GEG boosts green innovation by promoting the "innovation compensation effect" while inhibiting regional green development due to increased environmental costs, supported by the "compliance cost effect". The relationship between GEG and regional green development is moderated by economic development and environmental pollution in an inverted U-shaped pattern. When economic development and environmental pollution are at lesser levels, the positive effect of GEG amplifies as economic growth and environmental pollution ascend. However, if economic growth and environmental pollution continue to escalate, the positive impact of GEG will lose its strength. In low-carbon pilot cities, GEG negatively affects regional green development, whereas the opposite effect is seen in non-pilot cities. Regardless, there is an inverted U-shaped moderating impact of economic development or environmental pollution in both low-carbon pilot and non-pilot cities, yet the inflection point varies across regions. This paper offers an original perspective by creating a theoretical link between green development and environmental governance and emphasizes the significance of a nonlinear viewpoint when examining the relationship between variables in the regional green development process, thereby expanding our understanding of green development phenomena. [ABSTRACT FROM AUTHOR]

Developing green materials for infrastructure construction is of vital importance for sustainable development. Limestone calcined clay cement (LC3) is an appropriate alternative for the reduction of CO 2 emissions and resource consumption in the cement industry. Recycled concrete aggregate (RCA), seawater and sea sand are potentially advantageous from a sustainability perspective, especially in coastal regions. Fiber-reinforced polymer (FRP) bars with excellent corrosion resistance are ideal alternative materials for steel bars in a corrosive environment. In this paper, a new type of green concrete structure incorporating LC3, seawater, sea sand, RCA, and FRP bars was investigated, and the performance was assessed to verify the considerable sustainable advantages while considering life cycle cost and environmental impacts. The results showed that the proposed LC3 concrete exhibited lower environmental impacts: compared with traditional concrete, and the CO 2 and energy consumption of LC3 concrete were reduced by 32%–41.7% and 23.3%–28%, respectively. Although the initial cost was higher for LC3 and CFRP bars, this disadvantage could be offset by their longer service life and lower maintenance costs during their life cycle. Compared with traditional RC structures, the total cost, CO 2 and energy consumption in the life cycle were reduced by 39%, 30.5%–65.4%, and 18.5%–58.9% for the LC3 concrete structure, respectively. In contrast to the life cycle cost, the LC3 replacement ratio had a negative effect on the environmental indices for the LC3 concrete and structure. Considering the promising advantages in economy and environmental impact, the proposed LC3 concrete and structure provides an effective approach for the sustainable development of the construction industry. • A type of green concrete structure incorporating LC3, seawater, sea sand, RCA, and FRP bars. • LC3 concrete exhibits lower environmental impacts, decreased as the LC3 replacement ratio increases. • LC3 concrete structure shows excellent environmental and economic advantages. [ABSTRACT FROM AUTHOR]

With the deterioration of natural resources and the aggravation of environmental pollution, there is a growing imperative to implement industrial green reform through green technology investment (GTI). This paper constructs a nonlinear dual-objective function by drawing on the theory of two-sided matching under the carbon trading regulation, aiming to examine GTI selection decisions made by two oligarchic manufacturers (i.e., oligarchs) and a monopoly supplier (i.e., supplier). The supplier offers multiple green technologies, while oligarchs rack their brains to buy green technologies in an attempt to exploit their social, economic and environmental potentials. Available technologies vary in terms of their environmental efficiency and cost. Then, a two-sided matching mechanism is constructed to explore the availability of stable matchings and examine the impacts of the carbon price and green improvement coefficient on GTI, product pricing decisions and profits within a competitive environment. Finally, a Monte-Carlo experiment is designed to analyze the impact of uncertain market risks on supplier's decisions. The results show that there is a two-sided matching mechanism that enables oligarchs and suppliers to obtain stable matching solution sets. Higher carbon prices lead to higher product prices, lower market demand, and improved environmental performance. Product price and market demand are positively correlated with investment volumes of oligarchs themselves and negatively correlated with those of their competitors. Investment risk has a certain negative impact on suppliers' profitability. [ABSTRACT FROM AUTHOR]

• A joint energy-frequency regulation power market is proposed for energy transition. • Environmental cost of fossil-fueled generation is incorporated into electricity trading. • Renewable energy generation is motivated to actively manage uncertainty and intermittency of outputs. • Extensive numerical simulations are conducted to validate the proposed system. Striving to develop renewable energy generation (REG) has become a global trend. Due to high carbon emissions, fossil-fueled generation units (FFGUs) in an electrical energy market such as the day-ahead and real-time electricity markets are facing unprecedented constraints. From the perspective of FFGUs, an effective way is to change their market roles from energy suppliers with high environmental costs to secure and reliable frequency regulation service providers. To help FFGUs successfully complete the transition, a bi-level optimization model for the joint energy and regulation service market is proposed in this paper. Different from existing joint markets, both the environmental cost of FFGUs and the frequency regulation service (FRS) cost of REG units are considered in this research for market operators to determine the scheduling priorities of submitted bids from generation units. In the frequency regulation service market model, a dynamic approach to determining the FRS demand is applied, which not only incentivizes REG units to initiatively manage their outputs, but also derives the price signals through the equilibrium of the FRS demand and supply to ensure the revenue of FFGUs in the FRS market. Meanwhile, the decision-making model of the FFGUs under the proposed market mechanism is also presented. Finally, extensive numerical experiments demonstrate the feasibility and efficiency of the mechanism. Simulation results show that the proposed mechanism can stimulate a smooth transition of FFGUs by adapting to actual situations and policy changes. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs), i.e., drones, are expected to be widely used in various applications, such as parcel delivery and passenger transport, with the benefits of mitigating traffic congestion and reducing carbon emissions. In this paper, we study a UAV path planning problem under uncertain weather conditions, and design a data-driven dynamic decision support system for multiple types of UAVs. To this end, we categorize all relevant costs into three types, namely, economic, environmental, and social costs, and formulate a nonlinear two-stage stochastic programming model to establish optimal paths for UAV missions under weather uncertainty. We then discretize the nonlinear model and propose a tight linear approximation for the discretized problem to allow for a near real-time implementation. To quantify weather uncertainty, we propose a weather scenario generation algorithm to map ensemble-based weather forecast information to airspace blockage maps. With comprehensive computational studies through simulations, we show that our proposed stochastic approach can lower operating costs by an average of around 6%, where the savings increase as weather conditions become more severe and complex. We also find that, for missions operated by small UAVs, it is not sufficient to determine a path solely based on economic cost minimization, but it should rather be through total cost minimization, which involves environmental and social costs. Considering only the economic cost in the optimization may lead to much higher non-economic costs. However, for missions operated by large UAVs, it is sufficient to determine paths through economic cost optimization, as including environmental and social costs in the optimization process does not result in solutions that are much different from those obtained by considering only the economic costs. For both small and large UAVs, a path established solely through environmental or social cost minimization may not be economically sustainable, as doing so would imply very high economic costs. • Develop optimal UAV path planning solutions under evolving weather conditions. • Devise a weather scenario generation algorithm considering ensemble weather forecasts. • Integrate economic, environmental, social costs to ensure sustainable UAV operations. • Employ discretization and linearization techniques to solve the nonlinear problem. • Deliver practical insights to stakeholders with diverse objectives in UAV operations. [ABSTRACT FROM AUTHOR]