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In this chapter we present a continuous time model with reversible abatement capital in order to analyze the effects of environmental policies on the value of the firm and investment decisions. We show that the effects depend on what sort of future policy are implemented. We focus on investment effects of changes in corrective taxes to control the use of polluting inputs, and subsidies to promote abatement investment. We show that (1) while taxes have a depressive effect on capital accumulation, subsidies boost investment; (2) the impact of these policies on the value of the firm is ambiguous. This latter result has important empirical implications insofar as investment are based on the average value of the firm rather than the (unobservable) marginal value.

The main purpose of this paper is to empirically investigate the effect of macroeconomic uncertainty on environmental degradation in India over the period 1971–2016. Additionally, this paper considers the role of financial development, energy consumption intensity and economic growth in explaining the variation of environmental degradation in India.

The authors applied the power generalized autoregressive conditional heteroskedasticity model to measure inflation volatility and used it as a proxy for macroeconomic uncertainty. From a methodological perspective, the authors employ the autoregressive distributive lag bound testing model to establish the long-run equilibrium association between the variables. The Toda–Yamamoto causality approach has been used to examine the direction of causality between the variables.

Findings suggest that macroeconomic uncertainty exerts a positive effect on carbon emissions, indicating that higher inflation volatility, as a proxy for macroeconomic uncertainty, hinders India's environmental quality. Financial development, economic growth and energy consumption intensity have also adversely impacted environmental quality.

The negative association between macroeconomic uncertainty and environmental degradation calls for some stringent policy actions. While formulating policies to promote growth and maintain stability, policymakers and government stakeholders should take into account the environmental effects of macroeconomic policies. There is a need to implement more environmental-friendly technologies in the financial sector that could reduce carbon emission.

To the best of the authors' knowledge, this study is the first that considers the role of macroeconomic uncertainty along with financial development and energy intensity in an emerging economy like India.

The concept of geoeconomic time, introduced here, is based on the interface among economic activity, technology, and geophysical processes. Geoeconomic time is what should frame economic analysis of global warming policy. Otherwise, the analysis will be flawed. Application of geoeconomic time to global warming has two parts. The first is the future time frame for projecting impacts on the earth. The second is the historic time frame for delineating our bounds of ignorance regarding the possible consequences of global warming. Many economic analyses conclude against policies to reduce emissions of warming gases, and instead conclude in favour of the “optimal” policy of adapting to global warming. The concept of geoeconomic time reveals that the magnitude of our ignorance is of such a scale that we can never reduce uncertainty sufficiently to design “optimal” policies. Concludes in favour of risk‐reducing policies for research, development and commercialization of energy efficiency technologies and renewable energy. Successful examples can be found at the state and local levels of government action.

The paper presents a model of environmental resource management in an uncertain environment with measurement considerations. In particular, the model recognizes uncertainty about the dynamic dispersion relationships and in the measurement of emission rates and pollution levels. Since uncertainty enlarges the environmental resource management problem, actions that reduce uncertainty must be considered by the environmental control manager. Such considerations, however, cannot be pursued independently of pollution control actions. It is thus postulated that environmental policy issues must be analyzed in the context of an adaptive control problem with measurement considerations and a new adaptive environmental control strategy, which is obtained by introducing a function of the one‐period—ahead covariance matrix of the parameter estimates in the objective function, is suggested. This new strategy is shown to exhibit the dual effect of experimentation and caution.

This study explores the interconnectedness and complexity of risk-varied climate initiatives such as green bonds (GBs), emissions trading systems (ETS) and socially responsible investments (SRI). The analysis covers the period from September 2011 to August 2022, using six indices: three representing climate initiatives and three indicating uncertainty.

To achieve this, the study first examines dynamic lead-lag relations and correlation patterns in the time-frequency domain to analyse the returns of the series. Additionally, it applies an innovative approach to investigate the predictability of uncertainty measurements of climate initiatives across various market conditions and frequency spillovers in the short, medium and long run.

The findings indicate changing relationships between the series, increased linkages during turbulent market periods and strong co-movements within the network. The ETS is recommended for diversification and hedging against uncertainty indices, whereas the GB may be suitable for long-term diversification.

This study highlights the role of climate initiatives as potential hedges and contagion amplifiers during crises, with implications for policy recommendations and the asymmetric effects on market connectedness.

The paper answers questions that previous studies have not and contributes to the literature regarding financial risk management and social responsibility.

This paper, being complementary to existing perspectives, aims to examine the behaviors and the strategies of production migration of polluting firms from an economic point of view under appropriate decision conditions in terms of uncertain influence of supply chain support and green technology progress.

Strategic alternatives are investigated by using option pricing tools to examine the impact of various characteristics of green technology development and supply chain relations on the timing of the decision.

The theoretical and empirical results show that a polluting firm should not consider the option of relocating to offshore countries if uncertainty has been anticipated. It is suggested that, by facing green technology development uncertainty, a firm should be refrained from relocating production abroad if technology develops and offshore cost advantage disappears soon. On the other hand, a pre‐emptive migration strategy is preferable when the green technology is anticipated to be delayed.

A polluting firm in a supply chain faces challenges of uncertainty depending on whether it decides to produce domestically or to relocate internationally. The analysis conveys a concept that polluting firms can be more profitable by promoting clean production technology, instead of relocating to offshore or so called pollution havens.

The paper contributes to the existing literature on the evaluation of offshore migration option values by taking extra consideration of uncertainty in the supply chain cost, green technology progress and complementing to studies in a moral perspective.

The purpose of this study is twofold: first, to examine the role of non-governmental organizations (NGOs) in increasing customer environmental awareness (CEA) to decrease the municipal solid waste (MSW), and secondly, to examine the effect of government policies in the amount of air pollution caused by transfer stations (TSs).

This study proposes a mixed-integer nonlinear programming model. For solving this multi-objective problem, the authors use epsilon constraint method, which presented eight Pareto solutions. For selecting the best solution, the analytic hierarchy process approach is used. The presented model is applied on a real case study, and the results are discussed and sensitivity analysis is implemented on the parameters of the concern.

This study confirms the assumption that by allocating budget to NGOs for increasing CEA, the produced waste will be decreased.

In the present study, the authors only investigate air pollution caused by TS. Future studies can investigate other types of pollution. Furthermore, uncertainty in the amount of produced waste can be variable making the problem closer to the real environment. In this case, robust optimization may have better results.

Based on the results of sensitivity analysis, some implications obtain that can highlight by managers in the decision-making process. The operational costs of TS have a critical aspect in founding TS, so using new technology and high-tech machines for operational processes of TSs, can result in decreasing the running cost of TSs. Also, the determination of TS capacity is a remarkable issue in optimization, which should be paid special attention to this for the design of TSs in the planning phase of the system. Moreover, collaborating with NGOs has a good effect on increasing CEA that results in a decrease of MSW.

The role of NGOs and government simultaneity has been considered in a green supply chain. Moreover, the authors considered TS between source and disposal that reduce the time of transferring waste. Therefore, this study can be beneficial for the MSW management system, which faces the problems in the lack of capacity and transportation problems and environmental issues by proposing solutions in three studies including economic, environmental and social aspects.

The purpose of this investigation is the dynamic modelling of greenhouse gas (GHG) and air pollution emissions, to identify technology and policy options for reducing GHG and air pollution, and to explain how these options might affect the different variables of mobile source emission systems in Kosovo.

For modelling impacts of the technology and policy options for reducing GHG and air pollution, the model STELLA software has been used. The annual total emission for air pollutants (CO, NO_x, CH_x, SO₂ and dust) and GHG (CO₂) from the year 2000 up to 2025 is calculated. 2000 is taken as the base year for emission. Initial data value for vehicle population is taken from MEM and from World Bank ESTAP Project for Kosovo. Projection for the total number of vehicles in Kosovo is calculated with the WB Atlas Method, while the projection for emission factors and total annual emission for Air Pollutants and GHG (CO₂) are calculated with US EPA methodology.

From the results obtained using this model, the variables that drive GHG and air pollutant emissions and reduction in transport are identified. This model, predicts high emission of air pollutions and GHG in the short term from 2000 to 2010. After 2015, due to implementing the emission reduction policies and introducing new technologies in transportation, a continual reduction in air pollution will take place, whereas the CO₂ output up to 2025 will be reduced by 25 percent in comparison with the emission values of 2007.

Models presented here are the first, together with original data and results, with the predictions which are regional, but accepted globally. This work is original, since no such analysis has been carried out about mobile source emission systems in Kosovo. The paper provides data and results on which further research could be carried out.

This paper aims to conduct a fuzzy discrete time cost quality risk in the ambiguous mode CO_{2 tradeoff problem} (FDTCQRP*TP) in a megaproject based on fuzzy ground.

A combinatorial evolutionary algorithm using Fuzzy Invasive Weed Optimization (FIWO) is used in the discrete form of the problem where the parameters are fully fuzzy multi-objective and provide a space incorporating all dimensions of the problem. Also, the fuzzy data and computations are used with the Chanas method selected for the computational analysis. Moreover, uncertainty is defined in FIWO. The presented FIWO simulation, its utility and superiority are tested on sample problems.

The reproduction, rearrangement and maintaining elite invasive weeds in FIWO can lead to a higher level of accuracy, convergence and strength for solving FDTCQRP*TP fuzzy rules and a risk ground in the ambiguous mode with the emphasis on the necessity of CO₂ pollution reduction. The results reveal the effectiveness of the algorithm and its flexibility in the megaproject managers' decision making, convergence and accuracy regarding CO₂ pollution reduction.

This paper offers a multi-objective fully fuzzy tradeoff in the ambiguous mode with the approach of CO₂ pollution reduction.