Energy system transitions and macroeconomic assessment of the Indian building sector

ABSTRACT

India’s energy sector has grown rapidly in recent years with buildings playing a major role as they constitute about 40% of India’s final energy demand. This paper provides a quantitative model-based assessment of the evolution of India’s building sector in terms of both energy systems transition and its macroeconomic implications. The coupling of a bottom-up technology-rich energy system model with a macroeconomic computable general equilibrium (CGE) model provides an innovative approach for the in-depth robust analysis of the energy transition in India’s building stock and the induced macroeconomic and employment impacts on the Indian economy. Two main scenarios are explored, namely: the business-as-usual (BAU) and the advanced nationally determined contribution (Adv. NDC) scenarios. The investigation shows that efficiency improvements are vital to counteract the upward pressure on energy demand in the building sector. Energy demand in the building sector results in an increase of CO₂ emissions by 27% between 2015 and 2030 due to the technology transition from inefficient solid fuels (traditional biomass) to cleaner energy (liquefied petroleum gas (LPG), piped natural gas (PNG)) before shifting to electricity. The Adv. NDC scenario also leads to a shift in employment from agriculture and towards sectors that benefit from the implementation of Adv. NDC, especially in the construction sectors, electricity and manufacturing sectors.

KEYWORDS:

• building stock
• computable general equilibrium (CGE) model
• energy demand
• energy system model
• macroeconomic assessment
• nationally determined contribution (NDC)
• technology transitions
• India

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 About 840 million people.

2 Room air-conditioners (Building); frost-free refrigerators (Building); tubular fluorescent lamps; air coolers; colour televisions; electric geysers (Building); and distribution transformers.

3 Induction Motor; Agricultural Pump Sets; Ceiling Fans (Building); Domestic Liquefied Petroleum Gas (LPG) Stoves (Building); Washing Machine (Building); Computer (Notebook/Laptops) (Building); Ballast (Electronic/Magnetic); Office equipment's (Printer, Copier, Scanner, multi-functional devices [MFDs]) (Building); Diesel Engine Driven Monoset Pumps for Agricultural Purposes; solid State Inventor; Diesel Generator (Building); Variable Capacity Air Conditioners; and LED Lamps (Building).

4 Micro-level projections and the analysis of building sector according to climatic zones is outside the scope of this study.

5 The GEM-E3 model (Capros et al., 2013) has been extensively used for global and national energy and climate policy analysis and recently for the macroeconomic assessment of the European Union’s intended nationally determined contribution (INDC) (Fragkos et al., 2017).

6 In this case, the Adv. NDC scenario.

7 Data are consistent with International Energy Agency (IEA) statistics for 2015.

8 For a detailed model manual, including model equations and assumptions, for GEM-E3, see http://www.e3mlab.eu/e3mlab/GEM%20-%20E3%20Manual/GEM-E3_manual_2017.pdf/ .

9 For a detailed GEM-E3 model description, see Capros et al. (2013).

10 Armington assumption.

11 It includes carbon taxes, energy-efficiency standards and renewables support policies.

12 For additional details on GEM-E3 modelling of the building sector, see Karkatsoulis, Kouvaritakis, Paroussos, Fragkos, and Capros (2014).

13 The analysis does not simulate feedbacks from the global to the national levels; small impacts were expected on the energy system from limited changes in GDP (in line with Fragkos et al., 2017). Feedback effects could be implemented with additional iterations between AIM/Enduse and GEM-E3.

14 When the Indian NDC was included in the Paris Climate Change Agreement.

15 This target is not an official NDC target for India.

16 Financing of expenditure related to the NDC constrains the funds available for other investments and consumption purposes (‘crowding-out effect’). Crowding-out effects can diminish in the case a favourable financing scheme is assumed (E3MLab, 2016), i.e. in the case that firms and households can borrow in capital markets without facing increasing unit costs of funding, GDP impacts can be minimal and even positive. However, this is not examined in the current paper as it is considered outside the scope of the study.

17 As the carbon intensity-reduction target for 2030 is already achieved in BAU.

Additional information

Funding

This work was supported by the European Commission via the Modelling and Informing Low Emission Strategies (MILES) project, coordinated by the Institute for Sustainable Development and International Relations (IDDRI), Paris, and financed by the Directorate General Climate Action (DG CLIMA) [contract number 21.0104/2014/684427/SER/CLIMA.A.4].