Regional analysis across Colombian departments: a non-parametric study of energy use

Prevention, mitigation and adaptation to climate change from perspectives of urban population in an emerging economy

“…4) are deforestation (31.91%), inadequate solid waste management (26.59%) and air pollution generated by industries (21.18%) especially in the ranges from 25 to 44 years old indicating that causes of climate change are related to contamination in urban areas. These results concur to data of IDEAM that demonstrated a gradual decline of Colombian natural forest from an area of 56.4% in 1990 to 51.7% in 2014 and Pardo Martinez and Alfonso (2016) in the context of energy efficiency in manufacturing industries of Bogot a.…”

Data Envelopment Analysis in Energy and Environmental Economics: An Overview of the State-of-the-Art and Recent Development Trends

“…First, this review paper found there are various models of DEA have been used in previous studies. The important of DEA models were non-radial DEA (Wang et al [142]; Bian et al [66]), bootstrap DEA (Duan et al [120]), CCR and BCC models (Shi et al [80]; Mousavi-Avval et al [82]; Khoshnevisan et al [83]), DEA window analysis (Vlontzos and Pardalos [102]; He et al [115]), DEA frontier (Jan et al [78]; Lins et al [61]), VRS (Wang and Wei [81]; Zhou et al [97]), DDF (Vlontzos et al [154]; Wang et al [152]), DEA-Malmquist (Martínez and Piña [145]; Huang et al [137]; Wang and Feng [76]), SBM-DEA (Guo et al [108]; Chu et al [109]), DEA-MBP model (Welch and Barnum [72]), network DEA (Wu et al [67]; Yan et al [121]), stochastic DEA (Vaninsky [126]), stochastic network DEA (Chen et al [127]), SFA (Li and Lin [118]; ), radial stochastic DEA (Zha et al [132]), fuzzy dynamic network-DEA (Olfat et al [138]), CRTS and VRTS (Sueyoshi and Yuan [139]), DEA-DA (Chen et al [141]), fuzzy network SBM model (Shermeh et al [147]), Interval DEA-CCR (Gong and Chen [155]) and SE-DEA (Liu et al [159]). In addition, the results found that one previous review study classifies and review the recent DEA models under the methodological aspect, application schemes, efficiency measure, inputs, outputs.…”

Decomposition Analysis of the Aggregate Carbon Intensity (ACI) of the Power Sector in Colombia—A Multi-Temporal Analysis

“…Table 1 shows the review of papers recently reported specifically related to Colombia. Laverde (2021) [11] Economic Growth-CO 2 Vector Error Correction T.E Garces (2021) [12] Rural Electrification Case Study E.D Patiño (2021) [13] CO 2 emissions IDA-LMDI T.E Perez (2021) [14] Renewable Spot-Price Structural Model P.S Delgado (2020) [15] Decarbonization Global Change Analysis T.E Gutiérrez (2020) [16] Forecast Renewable Data P.S Pupo (2020) [17] Renewable Generation Data P.S Arango (2019) [18] Climate-Hydropower Partial Equilibrium P.S Valderrama (2019) [19] GHG Mitigation CO 2 Em Accounting T.E Pineda (2019) [5] Adaptation Composite Index P.S Pupo (2019) [20] Renewable Integration EnergyPLAN P.S Nieves (2019) [21] Energy Demand-GHG LEAP T.E Román (2018) [22] CO 2 emissions IDA-LMDI T.E Román (2018) [23] Energy Demand IDA-LMDI T.E Martínez (2016) [24] Energy Use Malmquist Analysis E.D Calderón (2016) [25] CO In [13], a decomposition analysis of carbon dioxide emissions of the global energy sector, including significant sectors such as transportation, is performed. As a mathematical model, the Kaya identity is used, which includes a carbon intensity factor, energy intensity, GDP per capita and population.…”