Buying green or producing green? Heterogeneous emitters’ strategic choices under a phased emission-trading scheme

Zhu, Lei; Chen, Lin-Ju; Yu, Xueying; Fan, Ying

doi:10.1016/j.resconrec.2018.04.017

Cited by 16 publications

(19 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Studying a combination of emissions trading, standards and research subsidies, Beckenbach et al (2018) find that relatively high permit prices with relatively low standards result in the lowest emissions, while subsidies have little impact. Zhu et al (2018) show that introducing boundedly rational agents to a carbon market can reproduce a pattern that is also observed in reality, namely that a permit price first overshoots the equilibrium value and then gradually declines. Lee and Han (2016) employ ABM to examine the costs of finding and selecting a suitable trading partner within a population of potential traders.…”

Section: Climate Policies In the Reviewed Studiesmentioning

confidence: 84%

“…When the demand side is modeled with consumers having heterogeneous preferences for “environmental quality,” firms choose their technology mix to compete in prices (as is usual) or in carbon intensity (Bleda & Valente, 2009; Desmarchelier et al, 2013). Other types of heterogeneity include distinct expectations about the profitability of investments (Kraan et al, 2018; Richstein et al, 2015; Zhu et al, 2018), different pricing rules due to specific mark‐ups or local monopolies (Wu et al, 2018), or even dissimilar R&D investment strategies (Chappin & Dijkema, 2009; D'Orazio & Valente, 2018; Isley et al, 2015; Lamperti et al, 2018). In addition, abatement options and costs may differ between firms (Lamperti et al, 2018; Lee & Han, 2016; Zhu et al, 2016; Zhu et al, 2018).…”

Section: Agents and Their Behavior In The Reviewed Studiesmentioning

confidence: 99%

See 1 more Smart Citation

A review of agent‐based modeling of climate‐energy policy

Castro

Drews

Exadaktylos

et al. 2020

WIREs Climate Change

View full text Add to dashboard Cite

Agent‐based models (ABMs) have recently seen much application to the field of climate mitigation policies. They offer a more realistic description of micro behavior than traditional climate policy models by allowing for agent heterogeneity, bounded rationality and nonmarket interactions over social networks. This enables the analysis of a broader spectrum of policies. Here, we review 61 ABM studies addressing climate‐energy policy aimed at emissions reduction, product and technology diffusion, and energy conservation. This covers a broad set of instruments of climate policy, ranging from carbon taxation, and emissions trading through adoption subsidies to information provision tools such as smart meters and eco‐labels. Our treatment pays specific attention to behavioral assumptions and the structure of social networks. We offer suggestions for future research with ABMs to answer neglected policy questions. This article is categorized under: Climate Economics > Economics of Mitigation

show abstract

Section: Climate Policies In the Reviewed Studiesmentioning

confidence: 84%

Section: Agents and Their Behavior In The Reviewed Studiesmentioning

confidence: 99%

A review of agent‐based modeling of climate‐energy policy

Castro

Drews

Exadaktylos

et al. 2020

WIREs Climate Change

View full text Add to dashboard Cite

show abstract

“…• GARCHs As for agents, the existing relevant literature mostly focused on four types of agents in the ETS system: the government (i.e., the ETS designer), enterprises (the ETS targets), third parties (the ETS regulators) and ETS market (the ETS platform). As for quantitative models, prevailing models for ETS research fall into six categories by quantification and solution: optimization models (game models [27,33], data envelopment analysis (DEA) [73][74][75], and other linear or nonlinear programming models [46,76]), simulation models (e.g., computable general equilibrium (CGE) [17][18][19], agent-based models (ABM) [1,5] and system dynamic (SD) models [25,48]), assessment models (e.g., analytic hierarchy process (AHP) [77][78][79], technique for order preference by similarity to an ideal solution (TOPSIS) method [80][81][82]), statistical models (e.g., difference in difference [83][84][85], GARCH processes [45,69], vector autoregressive (VAR) models [51,64]), AIs (e.g., artificial neural network (ANN) [54] decision tree (DT) [86][87], support vector machine (SVM) [26,44]) and ensemble models (e.g., AI-based optimization models [44,87], combined statistical and AI models [44,83] and decomposition and ensemble models [54]). Fig.…”

Section: Statistical Modelsmentioning

confidence: 99%

“…Using different models, the existing quantitative analyses have provided generally similar suggestions for enterprises, even with different prioritizations or to different extents. On the one hand, existing literature strongly recommended enterprises to actively adjust themselves to ETS (in terms of voluntary emission reductions for credits [136,137], technology investment [121], clean technology adoption [5], energy structure improvement towards cleaner energy [133], etc. ), as the top measure to preserve profitability in a long term, in view of the large benefit from selling excessive credits and reducing emission costs.…”

Section: Major Findingsmentioning

confidence: 99%

“…However, as a market-driven policy, ETS is inherently a typical complex system and has become one of the most challenging topics in the research fields of energy and climate economics [3]. On the one hand, unlike compulsory regulations (such as emission standards) directly targeting emitting behaviours, ETS regulates the emitters in a quite roundabout way through the market mechanism, with final efficacy impacted by a variety of uncertain factors [4,5]. On the other hand, rather than price instruments (such as carbon tax and subsidy) just imposing a cost or benefit on emissions, an ETS policy otherwise entails a series of rules and designs for different agents and markets, with each largely determining the mitigation effect [1,6].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quantitative models in emission trading system research: A literature review

Tang

Wang

et al. 2020

Renewable and Sustainable Energy Reviews

View full text Add to dashboard Cite

Diverse quantitative models have been applied to analyse emission trading system, as the top effective climate change policy. This paper is the first attempt to present a comprehensive literature review on full-scale types of quantitative models in emission trading system research. The models dominating emission trading system-related literature could be categorized as optimization models, simulation models, assessment models, statistical models, artificial intelligences and ensemble models. Using different quantification and solution tools, these models complemented and enriched each other in serving the various agents involved in emission trading system and facilitating their respective emission trading system related works: the government to design emission trading system policies, enterprises to participate in emission trading system and goods markets, third parties to regulate emission trading system and emission trading system markets involving different agents. For each agent, a systematic analysis is provided on research hotspots (the challenges to address), quantitative models (to describe the problems and find the results), main findings (the policy implications from the models) and future research (potential improvements on existing models). Some interesting conclusions are obtained. (1) Generally, China was the largest contributor to emission trading system research using quantitative models (representing 35.71% of the total articles). (2) The research hotspots were decision making by enterprises under an emission trading system (20.92%), spillovers amongst emission trading system and other markets (17.54%) and allowance allocation by the government (12.52%). (3) Popular quantitative models included various optimization models (32.00%) and simulation models (29.64%). Highlights：• A variety of quantitative models have been applied to ETS research.• A comprehensive review on full-scale types of models in ETS research is presented.• Optimization, simulation, assessment, statistical, AI & ensemble models were built.• Models helpfully served the government, enterprises, third parties & market in ETS.• For each agent, hotspots, models, main findings and future research are analysed.

show abstract

Corporate environmental responsibility in polluting firms: Does digital transformation matter?

Lin

Zhang

2023

Corp Soc Responsibility Env

View full text Add to dashboard Cite

The motivations for firms to undertake corporate environmental responsibility (CER) have been a hot topic in academia. However, with the rise of the digital economy, it is still been determined whether digital transformation will become a driving force for firms to assume CER. Based on panel data of Chinese listed firms in polluting industries, this article empirically tests whether and how digital transformation affects CER. The results show that digital transformation significantly motivates firms to assume more CER, especially for state‐owned and large‐scale firms and those located in the western region and ordinary‐level cities. The influence mechanism behind the relationship reveals that digital transformation improves financing ability and further encourages firms to assume CER. However, the effect is only short‐term and has no significant influence on the long‐term financing ability. Further research shows that regional differences in environmental governance pressure can strengthen the effect of digital transformation on CER. Our empirical findings provide policy implications for firms' digital transformation and sustainable development.

show abstract

Buying green or producing green? Heterogeneous emitters’ strategic choices under a phased emission-trading scheme

Cited by 16 publications

References 43 publications

A review of agent‐based modeling of climate‐energy policy

A review of agent‐based modeling of climate‐energy policy

Quantitative models in emission trading system research: A literature review

Corporate environmental responsibility in polluting firms: Does digital transformation matter?

Contact Info

Product

Resources

About