Dirty Energy Research Articles

SUBSTITUTION BETWEEN CLEAN AND DIRTY ENERGY WITH BIASED TECHNICAL CHANGE

AbstractThe elasticity of substitution between clean and dirty energy lies at the center of leading economic analyses of climate policy. Despite the importance, empirical assessments of this key parameter remain scarce. This article estimates the clean‐dirty elasticity of substitution using data on French manufacturing firms. Using the elasticity estimates, I then measure the bias in technical change at the firm level and find that technical change was largely biased toward dirty energy, despite a recent shift toward clean technologies. Finally, I recover the aggregate elasticity that combines substitution within firms and reallocation across firms from the micro elasticity estimates.

Renewable Energy Investments, Support Schemes and the Dirty Option

ABSTRACTIn a real options framework, we analyze the behavior of a large energy producer who can invest in a portfolio of Renewable Energy Source (RES) and dirty energy source. Competitive fuel prices challenge the investments in RES. Given a budget constraint, the agent allocates the optimal capacities of both energy instalments and selects the optimal investment time. We use the model to compare the effectiveness of classical support schemes such as Feed‐in Tariffs or Green Certificate with respect to forms of taxation of dirty technology such as Carbon Taxes or Carbon Permits. This paper proposes a conceptual framework and qualitative analysis to understand which support system enhances the attractiveness of renewable energy investments.

Environmentally clean and dirty energy equities during extraordinary global conditions

I examine which extraordinary international events coincide with pronounced changes in the equity markets for some of the world’s largest publicly traded suppliers on opposite sides of the global energy mix — oil and environmentally clean energy companies. First, I adapt an intuitively appealing non-parametric filter to empirically timestamp unexpected and prominent increases and decreases in a wide range of global indicators relevant to the international energy market. Then, I use such extraordinary conditions to characterise the performance of oil and environmentally clean energy equities, and their relationships. My findings suggest that jumps in the global stock market, international crude oil market shocks, and the US dollar real effective exchange rate, are the indicators that define the financial landscape during which considerable gains, losses, and instability across both types of energy markets materialise. In contrast, major elevated uncertainties related to geo-political risk and climate policy reflect relative stability in the equities of both oil and environmentally clean energy companies. Although these results imply that both energy assets are potentially lucrative hedging strategies for investors to exploit during heightened geo-political and climate policy uncertainties, clean energy equities offer market participants the option to combine profit maximising and sustainability objectives while minimising global energy security risks.

Does The Consumption of Energy, Renewable Energy, Natural Resources and Emissions Affect Gross Domestic Product?: The Study of Southeast Asia

We face the challenges of economic growth productivity which still uses dirty energy and minimal efforts to cleaner energy transition. The debate on efforts to increase productivity by diverting dirty energy into clean energy while still preserving nature has long been discused. Southeast Asia is a region that has massive development potential with valuable natural resources facing regional threats in addition to decreasing environmental quality but also prosperity. This study aims to identify initial steps are needed to control energy consumption, which is still considered dirty, the extent of the scope of Southeast Asia's current clean energy transition and how emissions can be a determinant of the decline in regional economic produtivity. This is the first study to combine the Cobb-Douglass production function model with three models that focus on (1) energy consumption, (2) renewable energy and natural resources, and (3) emissions. The data used is secondary data for the period 2004-2018 sourced from the World Bank Development Indicator and ourworlddata.org. Panel data regression was used as an analytical technique. This study was conducted in the Southeast Asia region (with selected countries with developing economies and insutries, namely Indonesia, Malaysia, Thailand, the Philippines and Vietnam). The results of this study show that in the energy consumption model, only labor has a significant effect on GDP, in model B there is renewable energy consumption and the labor force which significantly affects GDP and in model C there are CO2 emissions and GHC emissions that affect GDP in the Southeast Asia region. This research will provide input to policy makers and growth analysts to form policies for sustainable economic development in Southeast Asia.

Assessing the environmental impacts of clean energy investment in Pakistan using a dynamic autoregressive distributed lag model

In this study, the authors projected the impacts of clean energy investment on environmental degradation by applying a novel and dynamic Autoregressive Distributed Lag (DARDL) model for Pakistan from 1990 to 2022. Most researchers have used ecological footprint or CO2 emissions indicators to look at how clean energy investment affects environmental degradation, which primarily represents contamination induced by humans' consumption patterns and does not consider the impact of the supply side. Against this background, the study scrutinized the dynamic interaction between clean energy investment and environmental sustainability using the load capacity factor (LCF) as an ecological indicator in Pakistan, including economic growth, population density, trade openness, urbanization, and industrialization in the analysis. The long-run estimates from DARDL indicate that a 1 percent upsurge in clean energy investment mitigates environmental degradation by approximately 0.42 percent on average, controlling for other factors. Further, the study also revealed that a 1 percent increase in clean energy investment diminishes dirty energy consumption by approximately 0.45 percent. The validity of the findings is confirmed using alternate methods, i.e., KRLS. The study recommends that Pakistan prioritize investment in clean energy projects to promote environmental sustainability and enforce environmental regulations to reduce the adverse externalities associated with dirty energy activities.

Macroeconomic effects of carbon transition policies: An assessment based on the ECB’s New Area-Wide Model with a disaggregated energy sector

We assess the macroeconomic effects of carbon transition policies aimed at mitigating climate change in the euro area. To this end, we augment the ECB’s New Area-Wide Model (NAWM) with a framework of disaggregated energy production and use, which distinguishes between “dirty” and “clean” energy. Our central transition policy is that of a permanent increase in carbon taxes, which are levied as a surcharge on the price of dirty energy, combined with a price-preserving fall in fossil resource supply. Our findings suggest that increasing carbon taxes to an interim target level consistent with the transition to a net-zero economy entails a transitory rise in inflation and a lasting, albeit moderate decline in GDP. We show that the short and medium-term effects depend on the monetary policy reaction, the path of the carbon tax increase and its credibility, while expanding clean energy supply is key for containing the decline in GDP. Undesirable distributional effects can be addressed by appropriately redistributing the fiscal revenues from the carbon tax increase across households.

Do climate risks affect dirty–clean energy stock price dynamic correlations?

Prior studies have extensively exhibited an interest in exploring the connectedness between dirty and clean energy stock prices alongside the drivers of such price connectedness, shedding light on hedging strategies for finance practitioners. Nevertheless, no empirical research has examined whether climate risks, the emerging indicator for investors to handle the divestment of dirty energy stocks, have affected the time-varying dirty–clean energy equity price nexus. This study fills this gap by innovatively identifying dynamic conditional correlations (DCCs) between dirty and clean energy stock prices. An ARDL/NARDL model is applied to assess whether the climate risks affect such correlations by controlling for business cycles, funding liquidity, USD values, and oil market sentiments. Overall, we detect an undeniable negative impact of climate risks on the positive dirty–clean energy price dynamic correlations. Additionally, the NARDL model results reveal that a rise in federal fund rates exerts higher effects on the dirty–clean energy stock price comovements. Our findings suggest the strengthened potential of hedging clean energy stocks against dirty energy equities in case of escalating climate risks and heightened fossil fuel price volatilities. Furthermore, substantial attention is required to account for monetary policies' asymmetric effects on clean energy investment.

Drivers of Rural Households’ Choices and Intensity of Sustainable Energy Sources for Cooking and Lighting in Ondo State, Nigeria

Poverty reduction and the promotion of sustainable human development are fundamentally dependent on having access to modern energy services. Energy supplies that are dependable, reasonably priced, and sustainable are vital to modern societies. In achieving the sustainable development goals (SDG7) and access to clean energy supplies, this study, using cross-sectional data from 180 randomly sampled rural households, analyzed the key factors determining the choice and intensity of energy sources used for lighting and cooking in rural Nigeria. Both descriptive and inferential statistics (multivariate probit (MVP) and zero-truncated Poisson (ZTP models)) were employed for the analyses. The result showed that there is evidence of fuel stacking in their choice of cooking and lighting energy, and it increases with rising income levels but is more pronounced for lighting than cooking. The result also revealed that reliable access to clean energy (9% of sampled households for LPG and 23% of the households for grid electricity) is very low, as these households still rely on fuelwood (70%) for cooking, but the predominant usage of kerosene (39%) for lighting, as reported in the literature, has drastically changed to dry cell battery (51%). The results using a multivariate probit model to capture the multiple fuel usage phenomenon among rural households show that access to clean energy, improvement in rural poverty, usage of indoor kitchens, household size, and an increase in the education of household heads’ spouses significantly influence the use of clean energy in the rural areas. In the same vein, the result of the ZTP model showed that income, access to energy sources, and occupation of the household head were the drivers of the intensity of cooking and lighting energy sources. Thus, it is recommended that any policy interventions that are targeted at encouraging rural households to use clean energy should start by improving rural access to these clean energy sources, improving their poverty status while also increasing the level of education and awareness of rural women concerning the risks of using dirty energy sources.

How constant is constant elasticity of substitution? Endogenous substitution between clean and dirty energy

The degree of substitutability between clean and dirty energy plays a central role in leading economic analyses of optimal environmental policy. Despite the importance, assuming a constant and exogenous elasticity of substitution has been a dominant theoretical approach. We challenge this assumption by developing a dynamic general equilibrium model with an endogenous elasticity of substitution that interacts with the relative share of clean inputs in the economy. We find strong dynamic feedback effects arising from endogenous substitution capacity that amplifies the impact of directed technical change and accelerates the transition to a green economy.

Rural household nonagricultural income and energy transition: Evidence from central China

Promoting China's energy transition is essential for achieving low-carbon development and achieving the nation's carbon neutrality target. Previous research has primarily focused on industrial production energy consumption transition paths, and rural household energy consumption has rarely been investigated. This study focuses on the impact of nonagricultural income on rural household energy consumption in the Huang-Huai-Hai region using questionnaire data, examining the contribution of rural households' nonagricultural income to the energy consumption transition (i.e., from dirty energy sources such as firewood to clean energy sources such as liquified petroleum gas (LPG)). Instrumental variable two-stage least squares (IV-2SLS) estimation and IV-tobit models are applied to solve potential endogeneity problems. The results demonstrate that nonagricultural income can promote rural households' energy structure transformation, increase spending on clean energy such as LPG and electricity, and decrease expenditure on dirty energy sources such as coal and firewood. Further examination reveals that nonagricultural income can enable rural households to afford modern necessities and transition to cleaner, more efficient energy. The study proposes promoting rural energy structure transformation to boost sustainable development and increase nonagricultural income. Strengthening skills training, encouraging more off-farm employment, and other approaches will diversify rural households' income.

Connectedness and co-movement between dirty energy, clean energy and global COVOL

This paper examines dynamic connectedness and co-movement between dirty energy, clean energy and the global COVOL measure of Engle and Campos-Martins (2023) between January 2015 and May 2023 based on aggregated connectedness and wavelet coherence approaches. Our results show that COVOL and dirty energy are more closely linked in times of COVID-19 and geopolitical instability, while clean energy is more isolated from geopolitical events. Moreover, the coherence between dirty and clean energy markets is weaker after 2020, consistent with a decoupling hypothesis between dirty energy and clean energy.

Examining connections between the fourth industrial revolution and energy markets

We investigate the relationship between assets related to the fourth industrial revolution and alternatively dirty and clean energy markets. We examine portfolio diversification benefits and risk reduction gains based on three portfolio construction techniques. Results of static connectedness analysis indicate weak integration between the fourth industrial revolution assets and both clean and dirty energy markets with spillovers being mainly in the short term. Time-varying connectedness analysis shows that the connectedness is time-varying and responsive to external shocks Further, portfolio analysis shows that fourth industrial revolution assets are important for fixed-income portfolios that also comprise stocks and energy assets.

Tracing the dynamic impact of energy transitions on equity market volatility in an era of financial turbulence

This study investigates the dynamic relationship between the shift towards clean energy and stock market fluctuations, covering the period from January 2004 to August 2021 by applying time-varying parameters vector autoregressions (TVP-VAR). The results reported strong spillovers between clean energy markets and stocks, while dirty ones demonstrated disconnected spillovers. Asymmetry in the spillovers detected dominance of negative (bad) spillovers due to various uncertain economic and financial circumstances. The most significant negative (bad) events tracked in time-varying attributes are the global financial crisis, European Debt Crisis, Shale Oil Crisis, Brexit, US interest rate agenda, and the COVID-19 pandemic. Meanwhile, negative asymmetries prevailed in time-varying analysis, echoing the pre-eminence of negative crisis periods. We identified clean energy markets and stocks as net spillover emitters and dirty energy markets as net recipients. These findings have profound implications for a range of stakeholders, including investors, policymakers, and market participants, offering valuable insights for portfolio diversification and risk management strategies amidst the ongoing energy paradigm shift.

발전원간 대체탄력성에 대한 실증분석: 한국전력의 발전공기업을 중심으로

발전원간 대체탄력성에 대한 실증분석: 한국전력의 발전공기업을 중심으로

Air pollution impacts of amine scrubbing for CO2 capture

The current political discussion in the United States around carbon capture and storage includes statements that suggest a need for a technical review to clarify the expected air pollution impacts of amine scrubbing. The Center for International Law and 50 other organizations published an open letter claiming that “CCS is not consistent with the principles of environmental justice… CCS makes dirty energy even more dangerous for frontline communities. Facilities equipped with carbon capture technology have to burn more fossil fuel to get the same energy output, resulting in increased emissions of toxic and hazardous pollutants, like fine particulates (PM2.5).”This paper reviews air pollutants produced by the use of amine scrubbing on coal- and gas-fired power plants in the U.S. and the process features and mitigation strategies that will minimize their impact on air quality. Even with atmospheric reactions, emissions of amine, nitrosamine, and other air toxics are likely to have insignificant environmental and health impacts. Stacks will disperse emissions with a dilution factor greater than 8000. Water wash with or without acid will reduce emissions of amine and nitrosamine that is produced from atmospheric reactions. Nitrosamine emissions will be managed with selective catalytic reduction (SCR) to reduce the NO/NO2 and/or selection of primary or non-volatile amines. With coal-fired power plants, amine aerosols, Hg, SO3, and fine particulate emissions will probably be managed by a fabric filter with alkali addition. Carbon capture by amine scrubbing will reduce significantly the effect of the power plant emissions on ambient levels of PM2.5. With coal-fired power plants, the application of amine scrubbing will significantly reduce SO2 emissions. NOx emissions will usually be minimized by selective catalytic reduction (SCR) in both gas- and coal-fired plants. Ammonia emissions will be minimized by managing amine oxidation, and if necessary, by adding an acid wash or other controls.

Asymmetric Efficiency: Contrasting Sustainable Energy Indices with Dirty Cryptocurrencies

<p class="MsoNormal" style="margin-top: 12pt; text-align: justify;"><span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;">This paper examines the efficiency, in its weak form, of the clean energy stock indices, Clean Coal Technologies, Clean Energy Fuels, and Wilderhill, as well as the cryptocurrencies classified as "dirty", due to their excessive energy consumption, such as Bitcoin (BTC), Ethereum (ETH), Ethereum Classic (ETH Classic), and Litecoin (LTC), from January 2020 to May 30, 2023. In order to meet the research objectives, the aim is to answer the following research question, namely whether: i) the events of 2020 and 2022 accentuated the persistence in the clean energy and dirty energy indices? The results show that clean energy indices such as digital currencies classified as "dirty" show autocorrelation in their returns; the prices are not independent and identically distributed (i.i.d). In conclusion, arbitrage strategies can be used to obtain abnormal returns, but caution is needed as prices can rise above their real market value and reduce trading profitability. This study contributes to the knowledge base on sustainable finance by teaching investors how to use forecasting strategies on the future values of their investments.</span></p>

High Energy Consumption or Dirty Energy Using? The Driving Factors Behind Carbon Emission in National Supercomputing Centers

High Energy Consumption or Dirty Energy Using? The Driving Factors Behind Carbon Emission in National Supercomputing Centers

Constrained cooking energy choices in Tanzania: why urban dwellers cling on dirty even where clean energy alternatives are accessible?

PurposeThe purpose of this study is to examine households’ behavior towards dirty cooking energy utilisation in an environment where relatively higher accessibility to clean energy is noted. Although the low utilisation rate of clean energy can partly be attributed to utility gains anticipated in dirty energy mixes (DEMs) arising out of accessibility constraints, affordances and enablers, it is still unclear on the extend at which each of these contributes towards DEMs manifestation among the seemingly well-to-do households with higher levels of clean energy mixes (CEM) access. This study, therefore, hinges on scrutinising on this lower utilisation patterns despite a seemingly higher accessibility of CEMs, specifically liquified petroleum gases (LPG).Design/methodology/approachThe study is based on a household’s survey that was carried out in 2018, reaching a sample of 393 households using questionnaires in four wards of the Kigamboni district in Tanzania. Subsequent analyses were descriptive as well as inferential based on binary logistic regression analysis where utilisation of DEMs was predicted for both the high and low social economic status (SES) households by incorporating accessibility constraints, affordances and enablers.FindingsThe results show, first, if one assumes energy stacking is not an issue, as households become more constrained towards CEMs utilisation, they shift towards DEMs suggesting that the overall effect is a substitution, and second, the complementarity effect ultimately outweighs the substitution effect as households do not shift from DEMs to CEMs rather stack multiple energy. DEMs flourish in this case study area because those with high income are among those in the lowest SES, and some of those with the highest SES are from among the lowest income category, and all of them end up with more DEMs because shifting towards CEMs require income to complement SES.Practical implicationsPolicy-wise, removing hurdles in accessing CEMs such as LPG subsidy programme, gas stove provision to the poor, and enhanced LPG awareness will most likely benefits only those who do not stack energy in cooking while strategies targeting those at the lowest SES such as higher education attainment, empower women as a family decision maker, encourage co-occupancy to enlarge the household size and contain urban growth within certain perimeter will have a significant impact only if they raise both incomes and SES.Originality/valueDespite of the dominance of DEMs for cooking such as charcoal and firewood in Tanzania, CEMs such as LPG, have emerged as complements or alternatives in the household energy basket. The utilisation of such CEMs is, however, still very low despite the accessibility, cost, environmental and health advantages they offer. Accessibility is not the only factor fuelling CEMs; a complementarity must exist between SES and income for the positive transition towards CEMs to be realised.

Assessing the linkage of energy cryptocurrency with clean and dirty energy markets

The primary objective of this study is to explore the dynamic connectedness that exists among energy cryptocurrencies, clean energy metals, and conventional dirty energies. Moreover, we aim to investigate the time-varying effects of uncertainties on the connectedness that exists among these asset classes. We uncover heterogeneous reactions among energy cryptocurrencies and other assets. Common positive co-movement clustering is revealed mostly during the periods of the cryptocurrency crash and boom in 2018 and 2020, and the oil market crash, respectively. However, a constant weak and strong co-movement over shorter and longer horizons is observed throughout the sample period for all pairs analysed, with varying outcomes for longer horizons. Combining VIX and US EPU as measures of financial and economic uncertainty, we contend that uncertainty significantly affects energy cryptocurrencies, clean and dirty energies, and clean energy metals, with relatively weak impacts or influences observed for longer investment horizons. Our findings are highly beneficial to cryptocurrency investors, alternative asset investors, and policy makers, especially during periods of market turmoil.

Energy transition and climate change abatement: A macroeconomic analysis

The paper integrates the characteristics of regenerative energies into a dynamic macroeconomic model with climate change. Learning and economies of scale in new energy moderate the cost of emissions reductions and increase the speed of decarbonization. I provide closed-form analytical solutions for the development of regenerative energies, emissions, consumption, and population. The elasticity of substitution between clean and dirty energy inputs, stringency of climate policy, and potential raw material scarcity constitute critical conditions for reaching carbon neutrality by 2050. I find that a timely carbon phase-out requires sufficient substitution in the energy sector, continued learning and scale effects in regenerative energies, and active climate policy, which is indispensable even with enormous cost degression of regenerative energies. Raw material scarcity induced by regenerative energy use slows down the transition but can be overcompensated by more stringent climate policy at a moderate economic cost.