Socio-economic Implications of Mitigation Actions in the power sector including carbon taxes in South Africa Authors: B Merven, A Moyo, A Stone, A Dane & H WinklerDate: 5 November 2014

Presented at EconLab III, Cape Town

Background: policy questionsEmissions in South Africa are mainly from the electricity generation sector

±45% of total emissions in South Africa

According to the IRP Policy adjusted scenario, RE would contribute 14% to the electricity generating mix by 2030.

REIPPPP was launched to help contribute to the expansion of renewable energy

The South African government is planning to implement a carbon tax in 2016

It is important to align mitigation policies and measures with other national objectives.

The NDP puts the reduction of poverty and inequality as top priorities

The New Growth Path highlights the importance of job creation

Motivation for Linked Energy-Economy-wide Models

Need a tool that can measure the macro- and socio-economic impacts of Energy Policy

Available tools: Detailed bottom-up energy sector models Economy-wide models

But existing models approaches are inadequate Economy-wide Model (CGE type): over-simplification of the energy system Optimization Energy System Models: no/little economy and energy system feed-back

We chose the iterative linked modeling approach over full integration: Full inter-temporal integration constrains the level of detail Stakeholders like to see detail that they can relate to

Electricity Sector Model: SATIM-el Inter-temporal bottom-up partial equilibrium optimisation model of South Africa’s

energy sector (Energy Research Centre) SATIM-el: South African TIMES Model - Electricity Sector

Optimisation problem Minimize the sum of all discounted costs over the planning horizon subject to constraints and

system parameters Costs include capital costs, operating costs and taxes (e.g. CO2 tax) Constraints: electricity demand, resource limits, reserve margin, policy targets System Parameters: load curves, existing stock of power plants, new power plant options, fuel

price and availability Other: discount rate, taxes, etc.

SATIM-el: SATIM Calibrated and parameterised in line with recent Integrated Resource Planning Report

(update 2013) 20 time-slices, annual periods to 2040

Economy-wide Model: e-SAGE General equilibrium model of South African economy (SAGE, UNU-WIDER)

Recursive dynamic country-level economy-wide model eSAGE: detailed electricity sector

Comprehensive representation 62 industries 49 products 9 factors of production 14 representative households

Energy treated as an intermediate input (Leontief) Simplified energy-saving investment behaviour, which allow sectors of production to reduce

energy intensity in response to increasing energy prices constrained by the rate of investment in the sector

Upward sloping labor supply curves for less-educated workers and full employment for skilled labour

“Putty clay” capital and endogenous capital accumulation Fixed current account with flexible real exchange rate Savings-driven investment

e-SAGE-SATIM-el Iteration Processe-SAGE

SATIM-el

• Electricity demand• Fuel prices

• Electricity production mix by technology/fuel

• Electricity price• Power plant construction

expenditure schedule

SAGE

2010 2020 2030 2050

SATIM

2007

SAGE

SATIM

SAGE

Iter

ativ

e co

up

led

ru

ns

Committed Forecast

SATIM

TC

TT (IRP)

2010

2020

2030

Emulating the Planning (IRP) process

Convergence

2006 2010 2014 2018 2022 2026480

490

500

510

520

530

540

550

560

Coupled runs every year

Coupled runs two years

Coupled runs every four years

Year of coupled run

Ele

ctri

city

dem

and

in 2

030

(TW

h)

Baseline

Carbon Tax

NT CO2 tax and RE Scenarios and assumptions

Reference ScenarioPower sector without any mitigations actions or emissions constraints

A National Treasury (NT) CO2 tax Scenario

From $5 (R48)/ ton CO2 in 2016, increasing to $12(R120)/ton CO2 in 2025

Tax Recycled through reduction in sales tax

Two renewable energy programme scenarios, that is, aiming to reach20% share of centralised generation by 2030 and 30% in 2040 (RE Prog 1)

30% share of centralised generation by 2030 and 40% in 2040 (RE Prog 2)

In the ‘optimistic’ scenario (RE Opt) projected investment cost reductions for renewable technologies are aligned to the IRP 2010 (and the IRP update). In the ‘conservative’ scenario, there are halved.

Results: NT CO2 tax and RE Scenarios

0

50

100

150

200

250

300

350

400

450

2010 2015 2020 2025 2030 2035 2040

CO2

Emiss

ions

(Mto

n)

Reference

NT CO2 Tax

CGE-linked NT CO2 Tax

RE Prog 1

CGE-linked RE Prog 1

RE Prog 2

CGE-linked RE Prog 2

CO2 emissions from power sector with and without CGE Link

CGE-linked scenarios show slight demand response

Impact on economic growth by sector

Results: NT CO2 tax and RE Scenarios

All the policy scenarios would result in slight GDP loss in 2040, relative to the reference case

Impact is more severe with RE program 2 mainly because of the higher electricity price and higher capital intensity

Mining and metals sectors are the most negatively affected

However, in the RE scenarios, the electricity sector grows quite significantly

 2010 share of

GDP

Reference Scenario

Change in 2040 GDP relative to Reference (%)

average annual growth (%) NT CO2 tax

RE RE

Program 1 Program 2

Total GDP   3,08 -0,72 -1,46 -1,85

Agriculture 3,11 3,28 0,25 -0,68 -0,35

Industry 30,77 3,07 -1,19 -0,98 -1,12

Mining 8,83 3,61 -1,48 -4,82 -6,58

Manufacturing 16,83 2,76 -1,16 -1,84 -2,57

Other industry 5,11 3,01 -0,65 9,79 14,98

Electricity 1,81 2,86 0,12 36,07 54,52

Water distribution

0,59 3,04 -1,73 -2,73 -3,85

Construction 2,7 3,09 -0,86 -2,75 -3,9

Services 66,12 3,07 -0,56 -1,72 -2,27

Total number of new jobs created, 2010-2040 (1000)

  Difference in new jobs created by 2040 (%)

 New jobs created

in Reference(2010)

NT CO2 tax REProgram 1

REProgram 2

Total Labour 7 418 -2,6 -2,47 -3,87

Unskilled labour* 4 324 -4,47 -4,24 -6,65

Primary 1 431 -4,49 -4,02 -6,22

Middle 2 893 -4,45 -4,35 -6,86

* Skilled labour growth specified exogenously as stated in assumptions in the earlier section,

Results: NT CO2 tax and RE Scenarios

All the policy scenarios result in slight drop in employment in 2040, relative to the reference case for the less educated category (more educated is exogenous)

All

Poor (0-50)

Middle (50-90)

Top (90-100)

0 0.5 1 1.5 2 2.5 3 3.5 4

RE Prog. 2RE Prog. 1NT CO2 tax

Results: NT CO2 tax and RE Scenarios

Drop in per capita consumption in 2040 (%), relative to the reference

Drop in consumption due to slower growth

CO2 emissions from power sector with CGE Link

0

50

100

150

200

250

300

350

400

450

2010 2015 2020 2025 2030 2035 2040

CO2

Emiss

ions

(Mto

n)

CGE-linked Reference

CGE-linked $10 CO2 Tax

CGE-linked $10 CO2 Tax Opt. RE

CGE-linked $20 CO2 Tax

CGE-linked $20 CO2 Tax Opt. RE

CGE-linked $50 CO2 Tax

CGE-linked $50 CO2 Tax Opt. RE

Results: Carbon Tax Sensitivity Analysis

Tipping point for CO2 emissions somewhere between $10 and $20

tax levels.

Change in 2040 GDP relative to Reference (%)

Agriculture

Industry

Mining

Manufacturing

Other industry

Services

-20 -15 -10 -5 0 5 10

$50$20$10

Results: Carbon Tax Sensitivity Analysis

Includes Power Sector

Mining taking greatest hit

Results: Carbon Tax Sensitivity analysis

All

Poor (0-50)

Middle (50-90)

Top (90-100)

0 1 2 3 4 5 6 7 8

$50$20$10

Drop in per capita consumption in 2040 (%), relative to the Reference Scenario

Reduction in new jobs created by 2040 (%), relative to the Reference Scenario

$10 $20 $500

5

10

15

20

25

Unskilled labour

Future work includes considering redistributed RE.

Could also consider sensitivity to higher coal prices

We will also be integrating the other sectors to the CGE to allow for fuel switching

Consider other recycling options for tax revenues

Next Steps

Thank you

alfred.moyo@uct.ac.za

This document is an output from a project funded by the UK Department for International Development (DFID) and the Netherlands Directorate-General for International Cooperation(DGIS) for the benefit of developing countries. However, the views expressed and information contained in it are not necessarily those of or endorsed by DFID or DGIS, who can accept no responsibility for such views or information or for any reliance placed on them.