Improving the electricity efficiency in South Africa through a benchmark-and-trade system

Inglesi‐Lotz, Roula; Blignaut, James Nelson

doi:10.1016/j.rser.2013.11.028

Cited by 10 publications

(4 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First, some scholars use single-factor index to study electricity efficiency. Liddle, Inglesi-Lotz and Blignaut, Inglesi-Lotz and Blignaut, and Davies [27][28][29][30] measured and analyzed the electricity efficiency of a country or region by using electricity intensity (ratio between electricity consumption and economic output). Second, many scholars used the SFA approach to measure electricity efficiency.…”

Section: Electricity Efficiency Measurementmentioning

confidence: 99%

“…They also adopted the epsilon-based measure (EBM) model from the perspective of total factors to measure and evaluate the electricity efficiency of 30 provinces in China from 2006 to 2015. Inglesi-Lotz and Blignaut [29] proposed a benchmarking and trading system to reduce South Africa's electricity intensity and improve efficiency in an effort to reduce emissions in the country.…”

Section: Electricity Efficiency Measurementmentioning

confidence: 99%

See 1 more Smart Citation

Decomposition of Industrial Electricity Efficiency and Electricity-Saving Potential of Special Economic Zones in China Considering the Heterogeneity of Administrative Hierarchy and Regional Location

2021

View full text Add to dashboard Cite

Special Economic Zones (SEZs), an important engine of industrial economic development in China, consume large amounts of energy resources and emit considerable CO2. However, existing research pays little attention to industrial energy usage in SEZs and ignores the heterogeneity of administrative hierarchy and regional location. Considering the dual heterogeneity, this study proposes an improved two-dimension and two-level meta-frontier data envelopment analytical model to decompose the industrial electricity efficiency (IEE) and electricity-saving potential of SEZs in Guizhou Province, China, based on 4-year field survey data (2016–2019). Results show that the IEE rankings of three administrative hierarchies within SEZs are provincial administration SEZs, county administration SEZs, and municipality administration SEZs. The SEZs located in energy resource-rich areas and better ecological environmental areas have higher IEE than those in resource-poor areas and ecology fragile areas, respectively. This study can provide reference for policymakers to formulate effective policies for improving the electricity use efficiency of SEZs in China.

show abstract

Section: Electricity Efficiency Measurementmentioning

confidence: 99%

Section: Electricity Efficiency Measurementmentioning

confidence: 99%

Decomposition of Industrial Electricity Efficiency and Electricity-Saving Potential of Special Economic Zones in China Considering the Heterogeneity of Administrative Hierarchy and Regional Location

2021

View full text Add to dashboard Cite

show abstract

“…One exception applies to this projection. In the event that significant technological progress in combination with appropriate economic policy changes, as suggested in Inglesi-Lotz & Blignaut (2014), allows users to require or demand much less electricity to fulfil their energy needs, leading to an improvement in energy efficiency, the second of the two new coal-fired power stations may well prove to deliver excess capacity to the economy in the near term. However, if we are to replace the existing fleet of old coal-fired plants, the capacity of both new stations will be required in order to provide adequate base load.…”

Section: Conclusion and Policy Implicationsmentioning

confidence: 99%

An economy-wide evaluation of new power generation in South Africa: The case of Medupi and Kusile

Bohlmann

Inglesi‐Lotz

et al. 2016

Energy Policy

Self Cite

View full text Add to dashboard Cite

This paper investigates the role that the building of two new power stations, Medupi and Kusile, will play in facilitating future economic activity in South Africa. We use a dynamic computable general equilibrium (CGE) model to estimate the economy-wide effects of these new power stations. Our simulation results also provide insight into how much the local economy has lost due to inadequate electricity supply in the period leading up to the construction of Medupi and Kusile. We find that the decision to build additional power generation capacity was necessary and justified, and that the failure to sooner recognise the need for expansion of the country's electricity generation capacity and subsequent delays in commissioning Medupi and Kusile, likely cost the economy over R110bn in lost production. Additional analysis, in which a further two-year delay in the construction of Medupi and Kusile is simulated, shows that such an event will cause the economy to perform below baseline projections up to 2022.JEL Codes: C68, Q41, Q43

show abstract

“…This specifies that Capital Stock, Gross Domestic Product and Import Prices have an optimistic statistically significant influence on Mining Productivity/Output rate at 5% level, whereas a change in Electricity price and labour have an insignificant influence on Mining productivity/Output at 5% level. Various studies(Blignaut et al, 2015; Gosh, 2002;Inglesi-Lotz, 2014; Polemis and Dagoumas, 2013) support the…”

mentioning

confidence: 93%

Effects of Energy Pricing on the Mining Sector Performance in South Africa: An Econometric Approach

Mmutle

Daw

Khobai

2022

IJEEP

View full text Add to dashboard Cite

Mining plays a very substantial role in the economy of South Africa. This sector remains to be amongst the greatest consumers of energy in the country, especially electricity. It is against these background that the main objective of this study is to determine the effects of energy pricing on the mining sector performance in South Africa from 1990 to 2019. Johansen and ADRL procedures were used to determine the effects of energy pricing on the mining sector performance in South Africa. The results of the Error correction model (ECM) under Johansen cointegration technique are negative as expected but they are not statistically significant. The study error term is -0.122975, which meant that cointegration relationship is established. The results show that capital stock and labour play an important role in balancing mining productivity, while energy prices, Gross Domestic Product and import prices, on the other hand, play a lesser role in balancing mining productivity. The results of the Error correction model (ECM) under ARDL designate the short-run coefficient for D (LNCT) and D (GDP2) are statistically significant at 1% level and the coefficient of error correction term ecm (-1) valued at -1.037410 is negative and highly significant signifying that in the short-run changes in Capital stock and Gross Domestic Product are associated with mining production. The long-run relationship is illustrated by a negative sign on the coefficient of the ECT. Policy implication of a long-run optimistic relationship between electricity pricing and Mining Sector productivity is that mines should invest on producing their own energy, in that way they will be responsible for reducing their costs and that could results in mines increasing their productive capacity in the long-term.

show abstract

Improving the electricity efficiency in South Africa through a benchmark-and-trade system

Cited by 10 publications

References 20 publications

Decomposition of Industrial Electricity Efficiency and Electricity-Saving Potential of Special Economic Zones in China Considering the Heterogeneity of Administrative Hierarchy and Regional Location

Decomposition of Industrial Electricity Efficiency and Electricity-Saving Potential of Special Economic Zones in China Considering the Heterogeneity of Administrative Hierarchy and Regional Location

An economy-wide evaluation of new power generation in South Africa: The case of Medupi and Kusile

Effects of Energy Pricing on the Mining Sector Performance in South Africa: An Econometric Approach

Contact Info

Product

Resources

About