| Literature DB >> 35460358 |
Bishal Baniya1, Prem Prakash Aryal2.
Abstract
Many low-income countries (LICs), including Nepal, endeavour to deliver climate mitigation by reducing greenhouse gas (GHG) emissions and achieving more sustainable resource consumption. However, their prospects of delivering on such goals alongside the rapid structural changes in the economy prevalent in the LICs are not clear. This research aims to better understand the underlying complexity in the linkage between the framing of climate mitigation actions into government policies and the prospects for their delivery. We use critical discourse analysis, post-structural discourse analysis, and thematic analysis of textual data corpus generated from government policies (n = 12) and semi-structured interviews (n = 12) with policy actors, such as government policymakers and private sector and non-government organisations' representatives. We also develop energy and material consumption and GHG emissions models to predict their values up to 2050 via the R tools and machine learning algorithms that validate the accuracy of models. Our findings suggest that the social context of policymaking creates a knowledge structure on climate mitigation which is reflected in government policies. The policy actors and their institutions exchange their ideas and interests in a deliberative and collaborative environment to prioritise policies for the energy, forest, and transport sectors to deliver climate mitigation actions in Nepal. However, the energy sector, together with the agriculture sector, has insufficient climate mitigation actions. Reflecting on the high proportion of biomass in the energy mix and the rapid rise in fossil fuel and energy consumption per capita-both of which are driven by the remittance inflows-this research suggests measures to reduce these in an absolute sense.Entities:
Keywords: Climate mitigation; Energy and material consumption; Energy transition; Greenhouse gas emissions; Low-income country; Remittance
Mesh:
Substances:
Year: 2022 PMID: 35460358 PMCID: PMC9034080 DOI: 10.1007/s00267-022-01643-6
Source DB: PubMed Journal: Environ Manage ISSN: 0364-152X Impact factor: 3.644
Fig. 1Variables distribution (diagonal), the bivariate scatter plots with fitted lines, and correlation values with significance levels (***p < 0.05)
Scenarios and their description
| Scenarios | Description | TPEC and DMC | GHG emissions |
|---|---|---|---|
| Existing trend continues and the independent variables (GDP per capita, remittances, ODA, and agriculture’s share in an economic output) take their average annual growth rate from the last decade | GDP per capita (4.75%), Remittance (12.4%), ODA (9.9%) | GDP per capita (4.75%), TPEC per capita (2.3%), Agriculture’s share in economic output (−2.6%) | |
| The independent variables take their low growth rate values from the last decade | GDP per capita (2.9%), Remittance (4.8%), ODA (2.7%) | GDP per capita (2.9%), TPEC per capita (1.5%), Agriculture’s share in economic output (−7%) | |
| The independent variables take their high growth rate values from the last decade | GDP per capita (6.8%), Remittance (16.5%), ODA (19%) | GDP per capita (6.8%), TPEC per capita (4%), Agriculture’s share in economic output (−3%) |
Energy and material consumption models
| GDP per capita (GDP/cap) | Remittance (Rem) | Official Development Assistance (ODA) | Constant | Optimal shrinkage penalty (λ) | R2 (optimal cross-validated sum of squared residuals) | ||
|---|---|---|---|---|---|---|---|
| Total Energy Consumption | Unstandardized coefficients | 0.124 | 0.046 | 0.078 | 8.54 | 0.008 | 0.948 |
| Standardised coefficients | 0.346 | 0.423 | 0.191 | 0 | 0.094 | 0.947 | |
| Domestic Material Consumption | Unstandardized coefficients | 0.118 | 0.078 | 0.101 | 6.015 | 0.012 | 0.955 |
| Standardised coefficients | 0.245 | 0.530 | 0.184 | 0 | 0.095 | 0.955 | |
Greenhouse gas (GHG) emissions models
| GDP per capita (GDP/cap) | Energy per capita (Energy/cap) | Share of Agriculture (agrishare) | Constant | Optimal shrinkage penalty (λ) | R2 (optimal cross-validated sum of squared residuals) | ||
|---|---|---|---|---|---|---|---|
| Greenhouse gas (GHG) emissions | Unstandardized coefficients | 0.205 | 1.068 | −0.200 | −1.446 | 0.010 | 0.936 |
| Standardised coefficients | 0.399 | 0.449 | −0.098 | 0 | 0.094 | 0.940 | |
Fig. 2The shrinkage penalty (λ) value that minimises the mean squared error
Fig. 3Ridge trace plot to visualise the changes in standardised coefficients’ estimates as the shrinkage penalty (λ) chose its optimal value by minimising the mean square error
Fig. 4Predicted trend of total energy consumption, domestic material consumption and greenhouse gas emissions in Nepal up to 2050
Energy and material consumption and GHG emissions values for different scenarios up to 2050
| 2019 | 2030 | 2040 | 2050 | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Low-Value Scenario (LVS) | Existing Trend Scenario (ETS) | High-Value Scenario (HVS) | Low-Value Scenario (LVS) | Existing Trend Scenario (ETS) | High-Value Scenario (HVS) | Low-Value Scenario (LVS) | Existing Trend Scenario (ETS) | High-Value Scenario (HVS) | ||
| Total energy consumption (billion koe) | 12.9 | 13.2 | 14.8 | 16.6 | 14.3 | 17.9 | 22.2 | 15.5 | 21.5 | 29.6 |
| Domestic material consumption (million ktons) | 129.7 | 132.2 | 155 | 179.2 | 145.8 | 197.5 | 260.3 | 160 | 251.6 | 378.1 |
| Greenhouse gas emissions (million tons) | 54.3 | 80.9 | 82.9 | 106.8 | 116.3 | 121.8 | 197.4 | 167.2 | 178.9 | 365 |
Fig. 5% of the total domestic material consumption (Data source WU 2019)
Fig. 6% of the total energy consumption (Data source IEA 2021)