Abstract

The Colombian agricultural sector has the capacity and ambition to reduce its land use and GHG emissions through sustainable intensification of livestock production. However, the impact of such pathway on the availability of land for bioenergy crops production has not been thoroughly investigated. Moreover, previous assessments of the role bioenergy in Colombia have mostly focused on residues, in isolation of land use policies.To address this gap, we propose a hybrid statistical land balancing and suitability allocation approach to estimate long term projections of the cost–supply potential of bioenergy crops and residues. Regionalized to the 32 Colombian departments (administrative divisions), this approach could provide higher resolution than global assessments, while avoiding the complexity of spatially explicit methods. We investigated three scenarios covering the uncertainty of socioeconomic drivers and agricultural and livestock productivity factors.Our results suggest that pursuing progressive land use policies (SSP1 scenario) could release up to 14 Mha of land by 2050, which could be available to produce perennial bioenergy crops. The cumulative potential of crops in SSP1 could reach up to 2200 PJ, where about half of this potential could be attained at 7 $ GJ−1 or less. Potential supply centers could be identified in Orinoquía, Andean, and Caribbean regions for energy crops and the Pacific region for residues. Our findings indicate that there could be an opportunity to create synergy between the low carbon development strategies of the land use and energy sectors in Colombia.

Highlights

  • Our previous research results showed that some sectors such as Agriculture, Food and tobacco processing, and Textile industries have higher water dependency in terms of direct water consumption and water footprint (Zhao, 2019; Zhao et al, 2017); we identified the top five non-service sectors with highest direct water consumption and water footprint as the water supply constraint sectors to calculate the trade-offs and synergies of economic-social-environmental aspects triggered by water shortage

  • The highest loss of GDP was from scenarios B3 (Agriculture + Electricity and heating power production), T4 (Agriculture + Smelting and pressing of metals) and H6 (Agriculture + Smelting and pressing of metals), which were 10.8 billion Yuan (3.75 billion within capital region and 7.0 billion in the rest of China (ROC)), equal to 0.7% of Beijing’s GDP, 10.6 billion Yuan (4.07 billion within the capital region and 6.56 billion in the ROC), equal to 0.9 % of Tianjin’s GDP, and 89.4 billion Yuan (41.7 billion within the capital region and 47.7 billion in the ROC), equal to 3.7% of Hebei’s GDP, respectively

  • Our results indicate that of the total economic loss triggered by the imposition of water rationing in the capital region, 54% occurred in the capital region and 45% occurred in the rest of China

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Summary

Introduction

Based on MCDA, we selected the 10 best scenarios with regard to their economic, social and environmental performances as references for guiding future water management and suggested industrial transition policies This integrated approach could be a powerful policy support tool for 1) assessing trade-offs and synergies among multiple criteria and across multiple region-sectors under resource constraints; 2) quantifying the short-term supply-chain effects of different containment measures, and 3) facilitating more insightful evaluation of SDGs at the regional level so as to determine priorities for local governments and practitioners to achieve SDGs. mand, resource scarcity, environmental contamination and climate extremes, with the commitments to implement those by 2030 (Nerini et al, 2018). Many scholars have applied input-output or ecological network analysis to calculate virtual water trade across regions and sectors and quantify the distribution or allocation of water use through complex economic activities (Zhao et al, 2017; Zhao et al, 2015; Fang and Chen, 2015; Guan et al, 2014; Hubacek et al, 2009)

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