This article shows the potential impact on global GHG emissions in 2030, if all countries were to implement sectoral climate policies similar to successful examples already implemented elsewhere. This assessment was represented in the IMAGE and GLOBIOM/G4M models by replicating the impact of successful national policies at the sector level in all world regions. The first step was to select successful policies in nine policy areas. In the second step, the impact on the energy and land-use systems or GHG emissions was identified and translated into model parameters, assuming that it would be possible to translate the impacts of the policies to other countries. As a result, projected annual GHG emission levels would be about 50 GtCO2e by 2030 (2% above 2010 levels), compared to the 60 GtCO2e in the ‘current policies’ scenario. Most reductions are achieved in the electricity sector through expanding renewable energy, followed by the reduction of fluorinated gases, reducing venting and flaring in oil and gas production, and improving industry efficiency. Materializing the calculated mitigation potential might not be as straightforward given different country priorities, policy preferences and circumstances.
Key policy insights
Considerable emissions reductions globally would be possible, if a selection of successful policies were replicated and implemented in all countries worldwide.
This would significantly reduce, but not close, the emissions gap with a 2°C pathway.
From the selection of successful policies evaluated in this study, those implemented in the sector ‘electricity supply’ have the highest impact on global emissions compared to the ‘current policies’ scenario.
Replicating the impact of these policies worldwide could lead to emission and energy trends in the renewable electricity, passenger transport, industry (including fluorinated gases) and buildings sector, that are close to those in a 2°C scenario.
Using successful policies and translating these to policy impact per sector is a more reality-based alternative to most mitigation pathways, which need to make theoretical assumptions on policy cost-effectiveness.
Shape characterisation is important in many fields dealing with spatial data. For this purpose, numerous shape analysis and recognition methods with different degrees of complexity have so far been developed. Among them, relatively simple indices are widely used in spatial applications, but their performance has not been investigated sufficiently, particularly for building footprints (BFs). Therefore, this article focuses on BF shape characterisation with shape indices and classification schemes in a GIS environment. This study consists of four phases. In the first phase, the criteria for BF shape complexity were identified, and accordingly, benchmark data was constructed by human experts in three shape complexity categories. In the second phase, 18 shape indices were selected from the literature and automatically computed in GIS. The performance of these indices was then statistically assessed with histograms, correlation matrix and boxplots, and consequently four indices were found to be appropriate for further investigation. In the third phase, two new indices (Equivalent Rectangular index and Roughness index) were proposed with the objective to measure some BF shape characteristics more efficiently. The proposed indices also were found to be appropriate with the same statistical assessment procedures. In the final phase, BF shape complexity categories were created with the pairs of six appropriate indices and four choropleth mapping classification schemes (equal intervals, natural break, standard deviation, and custom) in GIS. The performance of the index–scheme pairs was assessed against the benchmark data. The findings demonstrated that both new indices and two of the selected indices (Convexity and Rectangularity) delivered higher performance. The custom classification scheme was found more ideal to reveal absolute shape complexity with the index value ranges derived from the boxplots while the other classification schemes were more appropriate to reveal relative shape complexity. 相似文献