Thailand Green GDP assessment based on environmentally extended input-output model

Kunanuntakij, Kultida; Varabuntoonvit, Viganda; Vorayos, Nat; Panjapornpon, Chanin; Mungcharoen, Thumrongrut

doi:10.1016/j.jclepro.2017.02.106

Cited by 60 publications

(42 citation statements)

References 21 publications

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“…Piaggio et al (2014) for example, examined the I-O tables of Uruguay to identify which economic sectors were the highest contributors of GHG emissions; I-O models with sensitivity analysis were also used to explore the impact of technological changes (Yuan and Zhao 2016) or identify the most sensitive factors (Yan et al 2016) on CO 2 emission intensities of the energyintensive industries in China. Kunanuntakij et al (2017) developed a Green GDP model for Thailand based on environmentally extended I-O model to create BAU trends. Yamakawa and Peters (2011) integrated structural decomposition analysis with I-O to study how each type of final demand (e.g., household, government, exports) affected the GHG emissions growth in Norway while Isaksen and Narbel (2017) evaluated the links between consumer expenditure in Norway with CO 2 emissions.…”

Section: Literature Reviewmentioning

confidence: 99%

Mapping a Low-Carbon Future for the Philippines: Scenario Results from a Fractional Programming Input-Output Model

Cayamanda

Aviso

Biona

et al. 2017

Process Integr Optim Sustain

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The Philippines submitted an intended nationally determined contribution (INDC) to the 2015 United Nations Climate Change Conference (COP21) which outlined a provisional commitment of 70% reduction in greenhouse gas (GHG) emissions by 2030 relative to the business-as-usual (BAU) levels. However, the basis of the proposed reductions is unclear, and the INDC has been criticized for possible adverse effects on the economic growth of the country. In this study, we make a rigorous highlevel assessment of potential for reduction in GHG emissions using a low-resolution fractional programming input-output model. Five scenarios are considered to gauge the lowest possible GHG emissions intensity per unit gross domestic product (GDP) in 2030 considering differentiated sector growth, changes in grid power mix, and broad deployment of end-use energy conservation measures. Our calculations show that the best projection from the combination of these low-carbon measures yields only a 30.6% reduction in emissions intensity relative to 2006 levels.

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Section: Literature Reviewmentioning

confidence: 99%

Mapping a Low-Carbon Future for the Philippines: Scenario Results from a Fractional Programming Input-Output Model

Cayamanda

Aviso

Biona

et al. 2017

Process Integr Optim Sustain

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“…Thirdly, despite the growing importance of emissions linked to consumption, evidence on Carbon Footprint (CF) of cities remains scarce and comparisons between territorial and CB accounts are largely absent from literature (Kunanuntakij et al, 2017). Therefore, this study could contribute to fulfill this gap.…”

Section: Introductionmentioning

confidence: 99%

Implementing city-level carbon accounting: A comparison between Madrid and London

Andrade

Dameno

Pérez

et al. 2018

Journal of Cleaner Production

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A change of focus from the current production-based Greenhouse Gas (GHG) emissions inventory to an integrated production-consumption carbon accounting system would improve the quality of city-level inventories. London is one of the few cities in the world which has reported its GHG emission inventory using Consumption-Based (CB) methodologies. Although Madrid has adopted the Global Protocol for Community-Scale Greenhouse Gas Emission Inventories (GPC) to report their GHG emissions, the city has never officially reported its GHG inventory using CB methodologies. Thus, the goal of this study is to analyze the challenges that must be faced by the production-based GHG emission inventory of Madrid in order to estimate GHG emissions from their supply chains and final consumers using the London's GHG emissions inventory experience. From the comparison between the 2010 GHG emission inventories of Madrid and London some challenges could emerge that Madrid's decision-makers might need to face regarding urban carbon accounting practices. As results, this paper shows that if GHG emissions from urban supply chains and final consumers are considered, the total GHG emissions reported by the Madrid inventory could double. This increase is similar to what London experienced when comparing production-based and integrated production-consumption GHG inventories. Besides, some difficulties related to lack of information were found when estimating GHG emissions associated with those goods and services consumed in Madrid. It would be useful to develop a full CB inventory using environmentally extended input-output (EEIO) matrices, more city-level primary data, and local cradleto-grave factors, following the methodology applied by the London inventory. These actions would allow a more accurate calculation of urban supply chains and final consumer GHG emissions in Madrid. Finally, further research is recommended in order to improve the current knowledge about the contributions of the energy, transport and waste sectors to GHG emissions in both, Madrid and London.

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“…For example, Sutton and Costanza [5] first attempted to map the ESV including green GDP accounting and estimated global market and non-market economic value and presented an integrated indicator called the subtotal ecological-economic product (SEP) that includes ecosystem services products (ESPs) and GDP. Kultida et al [6] calculated the green GDP for Thailand from 2015 to 2020 by considering three types of environmental costs. Lin et al [7] calculated the green GDP for China from 2006 to 2010 by considering energy intensity and pollution intensity.…”

Section: Introductionmentioning

confidence: 99%

“…It is obvious that green GDP accounting has become increasingly popular. Green GDP can be calculated by various methods concerning energy intensity, pollution intensity [7], environmental costs [6] and ecosystem service value [9,12,13]. Until now, using different methods to measure green GDP, comparing differences between green GDP and GDP and mapping the spatial distribution of green GDP have been widely studied concerning the historical value of green GDP.…”

Section: Introductionmentioning

confidence: 99%

National Green GDP Assessment and Prediction for China Based on a CA-Markov Land Use Simulation Model

Lin

et al. 2019

Sustainability

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Green Gross Domestic Product (GDP) is an important indicator to reflect the trade-off between the ecosystem and economic system. Substantial research has mapped historical green GDP spatially. But few studies have concerned future variations of green GDP. In this study, we have calculated and mapped the spatial distribution of the green GDP by summing the ecosystem service value (ESV) and GDP for China from 1990 to 2015. The pattern of land use change simulated by a CA-Markov model was used in the process of ESV prediction (with an average accuracy of 86%). On the other hand, based on the increasing trend of GDP during the period of 1990 to 2015, a regression model was built up to present time-series increases in GDP at prefecture-level cities, having an average value of R square (R2) of approximately 0.85 and significance level less than 0.05. The results indicated that (1) from 1990 to 2015, green GDP was increased, with a huge growth rate of 78%. Specifically, the ESV value was decreased slightly, while the GDP value was increased substantially. (2) Forecasted green GDP would increase by 194978.29 billion yuan in 2050. Specifically, the future ESV will decline, while the rapidly increased GDP leads to the final increase in future green GDP. (3) According to our results, the spatial differences in green GDP for regions became more significant from 1990 to 2050.

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Thailand Green GDP assessment based on environmentally extended input-output model

Cited by 60 publications

References 21 publications

Mapping a Low-Carbon Future for the Philippines: Scenario Results from a Fractional Programming Input-Output Model

Mapping a Low-Carbon Future for the Philippines: Scenario Results from a Fractional Programming Input-Output Model

Implementing city-level carbon accounting: A comparison between Madrid and London

National Green GDP Assessment and Prediction for China Based on a CA-Markov Land Use Simulation Model

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