Can the Framing of Climate Mitigation Actions into Government Policies Lead to Delivering Them? - Insights from Nepal's Experience

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.

Keywords: Climate mitigation; Energy and material consumption; Energy transition; Greenhouse gas emissions; Low-income country; Remittance.

Fig. 1

Variables distribution (diagonal), the bivariate scatter plots with fitted lines, and correlation values with significance levels (***p < 0.05)

Fig. 2

The shrinkage penalty (λ) value that minimises the mean squared error

Fig. 3

Ridge 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. 4

Predicted trend of total energy consumption, domestic material consumption and greenhouse gas emissions in Nepal up to 2050

Fig. 5

% of the total domestic material consumption (Data source WU 2019)

Fig. 6

% of the total energy consumption (Data source IEA 2021)