Product and corporate carbon footprint using the compound method based on financial accounts. The case of Osorio wind farms

Álvarez, Sergio; Sosa, María Belén; Sánchez, Alfonso Emilio Pérez

doi:10.1016/j.apenergy.2014.11.039

Cited by 18 publications

(3 citation statements)

References 40 publications

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“…Scholars have focused on the concept connotation (Xiao et al, 2019), calculation methods (Mancini et al, 2016), case studies of carbon footprints (Röös et al, 2013), etc. The calculation methods in these studies can be divided into "bottom-up" methods based on a process analysis and "top-down" methods based on an input-output analysis (Alvarez et al, 2015). The studies cover different scales, such as individuals (Kenny, 2009), families (Steen-Olsen et al, 2016), enterprises (Adewale et al, 2019), industries (Cadarso et al, 2015), cities (Chen et al, 2012), regions (Harris et al, 2012), and countries (Wolfram et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Spatial-temporal characteristics and decoupling effects of China’s carbon footprint based on multi-source data

et al. 2021

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In 2007, China surpassed the USA to become the largest carbon emitter in the world. China has promised a 60%-65% reduction in carbon emissions per unit GDP by 2030, compared to the baseline of 2005. Therefore, it is important to obtain accurate dynamic information on the spatial and temporal patterns of carbon emissions and carbon footprints to support formulating effective national carbon emission reduction policies. This study attempts to build a carbon emission panel data model that simulates carbon emissions in China from 2000-2013 using nighttime lighting data and carbon emission statistics data. By applying the Exploratory Spatial-Temporal Data Analysis (ESTDA) framework, this study conducted an analysis on the spatial patterns and dynamic spatial-temporal interactions of carbon footprints from 2001-2013. The improved Tapio decoupling model was adopted to investigate the levels of coupling or decoupling between the carbon emission load and economic growth in 336 prefecture-level units. The results show that, firstly, high accuracy was achieved by the model in simulating carbon emissions. Secondly, the total carbon footprints and carbon deficits across China increased with average annual growth rates of 4.82% and 5.72%, respectively. The overall carbon footprints and carbon deficits were larger in the North than that in the South. There were extremely significant spatial autocorrelation features in the carbon footprints of prefecture-level units. Thirdly, the relative lengths of the Local Indicators of Spatial Association (LISA) time paths were longer in the North than that in the South, and they increased from the coastal to the central and western regions. Lastly, the overall decoupling index was mainly a weak decoupling type, but the number of cities with this weak decoupling continued to decrease. The unsustainable development trend of China's economic growth and carbon emission load will continue for some time.

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Section: Introductionmentioning

confidence: 99%

Spatial-temporal characteristics and decoupling effects of China’s carbon footprint based on multi-source data

et al. 2021

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“…Wind power, one renewable energy option, is an attractive and clean source of energy, with environmentally friendly production yielding "green" power, so many institutions promote the use of wind power [2] [6]. However, manufacturing and constructing wind power generation systems have an indirect environmental burden [7][8][9][10][11][12][13][14]. As such, it is necessary to investigate the environmental impacts of the entire lifecycle of this renewable power generation system: manufacturing, construction, operation, maintenance, and disposal.…”

Section: Introductionmentioning

confidence: 99%

Hybrid input–output table method for socioeconomic and environmental assessment of a wind power generation system

2017

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paper title: "Environment, energy and economic analysis of wind power generation system installation with input-output table," No. 566). Nomenclature X Vector of domestic production I Unit matrix A Matrix of input coefficients F d Vector of domestic final demand E Vector of exports M' diagonal matrix with diagonal elements being import coefficients bij An element of the Leontief inverse matrix ej Backward linkage of sector j ri Forward linkage of sector i R Vector of added value P Matrix of added value coefficients Renergy Vector of energy consumption Penergy Matrix of energy consumption coefficients RCO2 Vector of CO2 emissions PCO2 Matrix of CO2 emissions coefficients A hybrid Matrix of input coefficients in hybrid model A* Matrix of commodity-by-commodity I-O technology coefficients C u Matrix representing upstream cutoff flows to the process data C d Matrix representing downstream cutoff flows to the I-O table from process data

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“…Wind turbines that operate in a wind farm are technologies which are capable of achieving these advantages (Alvarez et al, 2015). As a result, the installation of wind farms across the world has increased to 30% in the past decade (Daim et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Development of a quantitative analysis system for greener and economically sustainable wind farms

Simons

Cheung

2016

Journal of Cleaner Production

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Product and corporate carbon footprint using the compound method based on financial accounts. The case of Osorio wind farms

Cited by 18 publications

References 40 publications

Spatial-temporal characteristics and decoupling effects of China’s carbon footprint based on multi-source data

Spatial-temporal characteristics and decoupling effects of China’s carbon footprint based on multi-source data

Hybrid input–output table method for socioeconomic and environmental assessment of a wind power generation system

Development of a quantitative analysis system for greener and economically sustainable wind farms

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