Estimating the impacts of financing support policies towards photovoltaic market in Indonesia: A social-energy-economy-environment model simulation

Irsyad, Muhammad Indra al; Halog, Anthony; Nepal, Rabindra

doi:10.1016/j.jenvman.2018.09.069

Cited by 39 publications

(24 citation statements)

References 39 publications

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“…When SD is coupled with LCA, it adds temporal aspects to LCA (Peng et al, 2018), and LCA/SD coupling has been used to assess the economic and environmental performance of circular product systems (Asif et al, 2016). In another example of combined methods, EEIOA was used with ABM to study PV policies in Indonesia (Al Irsyad et al, 2019). SD and DES have also been combined, where SD enables the modeler to describe the system at a macro-scale, and DES can enrich this description with further details (Morgan et al, 2017).…”

Section: Combination Of Methodsmentioning

confidence: 99%

Do We Need a New Sustainability Assessment Method for the Circular Economy? A Critical Literature Review

Walzberg¹,

Lonca²,

Hanes³

et al. 2021

Front. Sustain.

112

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The goal of the circular economy (CE) is to transition from today's take-make-waste linear pattern of production and consumption to a circular system in which the societal value of products, materials, and resources is maximized over time. Yet circularity in and of itself does not ensure social, economic, and environmental performance (i.e., sustainability). Sustainability of CE strategies needs to be measured against their linear counterparts to identify and avoid strategies that increase circularity yet lead to unintended externalities. The state of the practice in quantitatively comparing sustainability impacts of circular to linear systems is one of experimentation with various extant methods developed in other fields and now applied here. While the proliferation of circularity metrics has received considerable attention, to-date, there is no critical review of the methods and combinations of methods that underlie those metrics and that specifically quantify sustainability impacts of circular strategies. Our critical review herein analyzes identified methods according to six criteria: temporal resolution, scope, data requirements, data granularity, capacity for measuring material efficiency potentials, and sustainability completeness. Results suggest that the industrial ecology and complex systems science fields could prove complementary when assessing the sustainability of the transition to a CE. Both fields include quantitative methods differing primarily with regard to their inclusion of temporal aspects and material efficiency potentials. Moreover, operations research methods such as multiple-criteria decision-making (MCDM) may alleviate the common contradictions which often exist between circularity metrics. This review concludes by suggesting guidelines for selecting quantitative methods most appropriate to a particular research question and making the argument that while there are a variety of existing methods, additional research is needed to combine existing methods and develop a more holistic approach for assessing sustainability impacts of CE strategies.

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Section: Combination Of Methodsmentioning

confidence: 99%

Do We Need a New Sustainability Assessment Method for the Circular Economy? A Critical Literature Review

Walzberg¹,

Lonca²,

Hanes³

et al. 2021

Front. Sustain.

112

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“…The optimization outputs are then categorized to monetary values and physical values of new power plant investments, electricity production, and loan interest payments. The monetary values are inputs for IOA, which use the Leontief Inverse Matrix that has 11 power plant technologies (Al Irsyad, Halog, & Nepal, 2019a). The physical values are included for LCA by multiplying the values with environmental factors.…”

Section: Methodsmentioning

confidence: 99%

The Impacts of Emission Reduction Targets in Indonesia Electricity Systems

Irsyad¹,

Halog

Nepal

et al. 2019

IJE

Self Cite

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Climate change policy often contradicts the least-cost objective of electricity generation in developing countries. The objective of our study is to propose electricity generation mixes that can meet emission reduction targets in Indonesia. We estimate the optimal generation mix, costs, and emissions from three scenarios, namely existing power plant planning, and 11% and 14% emission reductions in Indonesia’s electricity sector. The estimations are based on linear programming, input-output analysis, and life-cycle analysis, integrated into an agent-based modeling (ABM) platform. The simulation results confirm the existing power plant planning, which is dominated by coal-based power plants, as the lowest-cost scenario in the short-term; however, this scenario also produces the highest emissions. Emission reduction scenarios have lower emissions due to a higher share of renewables and, therefore, the Indonesian electricity system is robust from fossil fuel price increases. In the long-term, costs incurred in the emission reduction scenarios will be lower than electricity generation costs under the existing power plant planning. Our findings should be a basis for re-evaluating energy policies, power plant planning, and the research agenda in Indonesia. Keyword: linear programming, agent-based modelling (ABM), input-output analysis, life-cycle analysis

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“…Agents usually interact in an explicit social network. Psychologically more complex behaviors are more common in models with a limited scope, such as describing the adoption and diffusion of a single option—for example, an electric vehicle or solar PV on one's roof (Irsyad et al, 2019; Kangur et al, 2017; Natarajan et al, 2011; Schröder & Wolf, 2017; Silvia & Krause, 2016). On the other hand, ABM studies describing the entire economy tend to include simpler behaviors (Lamperti et al, 2018; Monasterolo & Raberto, 2016).…”

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

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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

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Estimating the impacts of financing support policies towards photovoltaic market in Indonesia: A social-energy-economy-environment model simulation

Cited by 39 publications

References 39 publications

Do We Need a New Sustainability Assessment Method for the Circular Economy? A Critical Literature Review

Do We Need a New Sustainability Assessment Method for the Circular Economy? A Critical Literature Review

The Impacts of Emission Reduction Targets in Indonesia Electricity Systems

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

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