Achieving sustainable development in India along low carbon pathways: Macroeconomic assessment
Introduction
The developing Indian economy faces multiple challenges echoing key dimensions of the Sustainable Development Goals (SDGs) of the United Nations. It thus counts around 269 million people living in poverty (Planning Commission, 2013), approximately 500 million deprived of clean cooking fuel, 304 million having no access to electricity (NEP, 2017), 163 million without access to safe drinking water (WaterAid, 2018), close to 1.7 million people homeless (Census, 2011) and 48% of rural households lacking basic socio-economic services (SECC, 2011). Moreover, post-2020 climate commitments outlined in India’s Nationally Determined Contribution (NDC) under the Paris agreement of the United Nations Framework Convention on Climate Change (UNFCCC) envision development along low-carbon emission pathways. India’s enormous developmental needs therefore have to be balanced with emission reduction targets. The fact that coal produces nearly three fourth of electricity generated in India points at potentially high costs of emission control. In such conditions, achieving rapid economic growth and GHG mitigation targets concurrently can have substantial macroeconomic implications. Though climate action can help redress the trade unbalance via reduction of large fossil fuel imports, the transition to non-fossil fuels could be costly.
India has been growing at a remarkable GDP growth rate of 7–8% annually since economic liberalization in 1991. The share of agriculture in GDP has gone down from 42% in 1970 to 17% in 2015, and continues to decline. In 2016, services and industry sectors constituted 53% and 31% of GDP respectively (Economic Survey, 2018). The Indian manufacturing sector is expected to contribute more to GDP with implementation of government policies like Make in India, Smart Cities Mission and Housing for all. While the services sector has grown in the past much more than the manufacturing sector, programmes like Digital India, Start-up India and several other social programmes are likely to support the growth rate of service sector in future as well. Initiatives like increasing domestic production and substituting crude oil with cleaner fuels like bio-fuels have been taken recently with the idea of reducing dependence on crude oil imports, which are responsible for large trade deficit and raise energy security issues.
India has committed to reducing the GHG emission intensity of its GDP by 33–35% from 2005 levels as well as to raising the non-fossil contribution to its power generation capacity to 40%, by 2030 (MoEFCC, 2015). To meet these targets, the Government of India (GoI) has taken several measures under the National Action Plan on Climate Change (NAPCC). It has set the target of building 175 GW of renewable power capacity, including 100 GW of solar power, by 2022. It has put emphasis on improving energy efficiency via several demand-side management initiatives and strict norms for the energy-intensive industries. Further, the GoI has taken a joint initiative with State Governments to provide 24 × 7 Power for All (PFA) by 2022. Though coal dominance as primary source of power generation in India is expected to continue in the near term, the government has rolled out clean coal policies to improve the efficiency of coal power plants (NEP, 2017). On the front of transport, it is promoting electric and hybrid vehicles through financial incentives, and imposing higher vehicle efficiency standards.
The motivation of this paper is therefore to capture the macroeconomic and energy implications of achieving development along low-carbon pathways. Several studies already investigate Indian mitigation pathways with focus on the transition of energy systems and its costs (Fragkos and Kouvaritakis, 2018, Dubash et al., 2015, Lucas et al., 2013, Chaturvedi and Shukla, 2014, Shukla et al., 2010, Shukla et al., 2015, van Ruijven et al., 2012). Some papers assess the implications of mitigation pathways for specific sectors like transport (Dhar et al., 2017, Dhar et al., 2018) or renewable energy supply (Shukla and Chaturvedi, 2012, Mittal et al., 2016). With regard to methodology, some papers adopt one of the two approaches- bottom-up and top-down, while others apply integrated analysis. On the pure bottom-up front, Kumar and Madlener (2016) use the LEAP (Long Range Energy Alternatives Planning system) model to explore the challenge of reducing Indian power sector’s coal intensity via deployment of renewable alternatives, in the face of rapidly increasing demand. Vishwanathan, Garg, Tiwari, and Shukla (2018) explore the opportunities and challenges involved in achieving the mitigation goals associated with 2°C and well-below 2°C caps to global temperature increase using the AIM (Asia Pacific Integrated Model) Enduse model. However, by their bottom-up nature, both AIM/Enduse and LEAP models ignore any feedback of energy costs on energy demand or the wider economy, either through consumption or investment markets.
On the top-down front, analysis mostly builds on applications of multiregional Computable General Equilibrium (CGE) modelling tools. van Ruijven et al. (2012) extensively survey pre-2012 studies using CGE. Recent studies like Mittal, Liu, Fujimori, and Shukla (2018) use the India version of the AIM (Asia Pacific Integrated Model) CGE model to determine the GDP costs of mitigation. Planning Commission (2014) assesses the costs of adopting the low-carbon, inclusive growth strategy using a model that has certain bottom-up technology information embedded within a top-down framework. Parikh (2012) provides a strategy to achieve sustainable development along low-carbon pathways in India using a top-down econometric model and Integrated Energy Systems model. These models partially address the call for a better control of the interface between economic and technical systems (Hourcade, Jaccard, Bataille, & Ghersi, 2006) by improving their descriptions of energy supply, including via explicit mixes of discrete technologies. However, the rest of their structures remains anchored in the CGE paradigm, ill adapted to modelling either the inert, complex dynamics of energy demands or the specific constraints that development requirements exert on energy transition dynamics (Edenhofer, Pichs-Madruga, Sokona, Farahani, Kadner, & Seyboth, 2014). They also stick to the uniform pricing rule, which forbids proper treatment of heterogeneous pricing of homogeneous goods—typically, electricity or natural gas (Le Treut, 2017). The calibration source common to many of them, the GTAP database, departs from first-hand national sources through the statistical treatment required to balance international trade. The second-best features, of developing economies especially, such as administered prices, wages and exchange rate control elude the grasp of their implementation of the CGE paradigm.
One particular instance of the Institute of Economic Growth (IEG) CGE model of Pradhan and Ghosh (2012) stands out by specifically focusing on the Indian economy. Notably, it builds on an original Social Accounting Matrix (SAM), and pays attention to the macroeconomic dimension of mitigation pathways by testing alternative closure rules. It also combines with bottom-up model in a commendable effort to articulate the national and international scales of analysis (Weitzel, Ghosh, Peterson, & Pradhan, 2015). However, it falls short from accommodating explicit physical energy statistics, either in its SAM or in modelling specifications. Johansson et al. (2015) suffers from similar calibration drawbacks for assessing the economic and energy implications of limiting the global temperature increase at 2°C above pre-industrial level by employing top-down IEG-CGE and bottom-up MARKAL (MARKal ALlocation) India models along with global models. Last but not least, Shukla, Dhar, and Mahapatra (2008) deploy a soft-coupling strategy combining the strengths of bottom-up (AIM/CGE) and top-down (MARKAL) approaches to explore low-carbon futures for India. However, the linkage only consists in the one-way feeding of AIM demand drivers into MARKAL, without any feedback in the form of, e.g., updated energy costs and attached investment requirements, thus only partially addressing consistency issues.
Our analysis of Indian low-carbon pathways focuses on filling up these methodological gaps. We couple the Asia-Pacific Integrated Model (AIM)/Enduse bottom-up optimization model to the top-down economy-wide IMACLIM-IND model calibrated on original data reconciling national accounting and energy balance statistics. This method provides distinct advantages of analysing the energy-economy impact of interaction between mitigation policies and assumptions about structural change in the economy. Iteration to convergence warrants full consistency between the macroeconomics of IMACLIM-IND and the energy-systems description of AIM/Enduse. The model makes use of up-to-date information on energy systems and economy (see Section 2).
With this “hybrid” tool, our primary objective is to contribute to national climate policy making by determining the macroeconomic implications of implementing the mitigation policies along with other socio-economic objectives. Our research focus is on improving the diagnostics about the policy questions on mitigation pathways in the Indian context. The need to design such policy packages that meet mitigation and development goals simultaneously is also highlighted in the fifth assessment report of the IPCC (Edenhofer et al., 2014). We inquire into the uncertainties involved at the interface of technological constraints in low-carbon pathways, and macroeconomic structural changes. This allows us to map the synergies and trade-offs associated with achieving the mitigation targets and economic goals simultaneously.
The rest of our article divides in 4 sections. We detail our modelling methodology in Section 2. In Section 3, we describe four mitigation and development scenarios followed by comments on scenario results in Section 4. Finally, we conclude and derive policy implications in last section.
Section snippets
Methodology
The limitations of conventional top-down and bottom-up approaches of energy-economy modelling have been the focus of attention for years. While the top-down approach lacks technological explicitness, bottom up models fail to integrate macroeconomic feedbacks and microeconomic behaviours in their analysis (Grubb, Edmonds, Ten Brink, & Morrison, 1993). Reconciling both approaches in energy/economy modelling is critical to producing comprehensive assessments of the expected impacts of mitigation
Scenario architecture
We model and project at 2030 and 2050 milestones four scenarios comprising one business as usual (BAU) scenario and three scenarios of further carbon constraint, in line with potential Indian contribution to global action targeting a 2°C cap on global temperature increase, differentiated by lower, middle (BAU) and higher growth dynamics. The purpose of this differentiation is to determine the feasibility and related trade-offs of achieving high growth in a low-carbon economy.
Results and discussion
We first explore the energy dynamics for our reference scenario (BAU), which emulates the current policy, socio-economic and energy prospects. We find that coal dominance persists from base year 2012 (BY2012) up to 2050, although the contributions of renewables,4 biomass and natural gas increase as a result of
Conclusion and policy implications
This article applies the coupling of the IMACLIM-IND economy-wide and the AIM/Enduse energy systems models to determine the implications of low-carbon pathways on India’s economic development. We draw the following conclusion and policy implications from our analysis. First, low-carbon policy measures like stringent energy-efficiency requirements, promotion of electric vehicles and shared transport, switch to non-fossil power generation and incentives for behavioural adjustments are compatible
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
We sincerely thank the two anonymous reviewers for their valuable comments and suggestions. This research was partially funded by DDP BIICS project with IKI number 18_I_326_Global_A_Climate Action After Paris. Development of the IMACLIM model benefits from support of the Chair Long-Term Modelling for Sustainable Development (Ponts Paristech-Mines Paristech) funded by Ademe, GRT-Gaz, Schneider Electric, EDF, RTE, Total and the French ministry of Environment.
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