Rapid economic growth, without climate change mitigation in the GWC scenario, results in a 6.3-fold increase in emissions from the base year to 2050. If the city is slow to respond to climate change as in the Slow Go City, emissions will increase by 2.5 times from the base year.
Background
Therefore, in addition to the Kyoto Protocol, South Africa has also implemented a number of national policies and strategies that address climate change issues. It is also essential to mitigate the impacts of climate change on cities because a large proportion of anthropogenic GHGs are emitted as a result of urban activities (Satterwaithe, 2005).
Motivation for study
This will enable them to assess possible mitigation and adaptation measures for the area (Fisher et al., 2007). However, simply predicting GHG emissions is highly uncertain because future emissions depend on a complex system, which cannot be fully understood (Nakicenovic et al., 2000a).
Aim
The City of Durban has been actively involved in mitigating global warming through reducing energy consumption, which is highlighted in its Integrated Development Plan (IDP) (Ethekwini Municipality (EM), 2008), Long Term Plan (Imagine Durban, 2009) and through the implementation of the eThekwini Energy Strategy. Meeting these goals would also stimulate an increase in energy demand and thus GHG emissions and as a result are in conflict with the city's plans to reduce GHG emissions.
Objectives
However, the city also strives to promote development in various economic sectors, promote economic growth and increase the supply of infrastructure, housing and other services in the city (Economic Development Unit, 2008 and EM, 2008). Therefore, it is important to understand the consequences of different urban development paths on future energy consumption and related greenhouse gas emissions in order to strategically plan and mitigate future emissions.
Scope of the dissertation
Chapter four describes the methodological approach to the study, including the process followed, the data that was collected, a description of the scenario accounting tool used and the limitations of the study. Chapter five presents the results and discussion of the study by describing the scenarios that were created by downscaling global IPCC scenarios to the local level and the scenarios that were developed based on the different storylines.
Introduction
Global Scenarios
- The IPCC Special Report on Emission Scenarios
- World Energy Outlook
- International Energy Outlook
- Energy Policy Scenarios to 2050 (WEC, 2008)
- Shell Energy Scenarios to 2050
- Comparison of global scenarios
This is due to society's greater environmental concerns in this scenario (Nakicenovic et al., 2000a). Finally, the IPCC stated that the scenarios are consistent with historical data and with projections from other forecasts and scenarios (Nakicenovic et al., 2003).
Smaller scale scenarios
Regional scenarios
The model uses a top-down and bottom-up approach to develop its scenarios (Jiang et al., 2000). The top-down approach uses an assessment of the balance of energy supply and demand to determine energy prices and energy efficiency improvements (Jiang et al., 2000).
Local scenarios
In the United Kingdom, past scenarios have been developed by the Department of Trade and Industry called the Foresight Scenarios. The Foresight Scenarios were based on the IPCC scenarios, as mentioned earlier (UK Department of Trade and Industry, 1999).
Driving forces of emissions
- Population
- Economic growth and development
- Technological change
- Other drivers of emissions
This is a major contributor to a decline in population growth rates in South Africa (Dorrington et al., 2006). Technology is another important driver of energy consumption and GHG emissions (Fisher et al., 2007; . Winkler, 2009).
Discussion and conclusion
The rate of technological diffusion in an economy is influenced by the cost of the technology and the level of investment in R&D. The main factors that drive emissions and are taken into account when creating scenarios are economic developments, including structural changes, population growth and technological changes that affect fuel composition.
Introduction
Context
GHG emissions and energy consumption
- Industrial and commercial sectors
- Transportation sector
- Residential sector
- Local authority
Another contributor to the large amounts of GHG emissions is the consumption of coal in the industrial sector (17%). According to Winkler (2009), the transport sector is the fastest growing contributor to GHG emissions in the country.
Economic growth
Future economic development in Durban
Economic development and GDP growth in the city will inevitably result in an increased demand for energy resources, resulting in a higher output of GHG emissions. However, according to the Economic Development Strategy (Economic Development Unit, 2008), it is predicted that 60% of future growth will be in the service sectors. This will result in a structural shift in the economy, which in turn will affect future GHG emissions.
Population and households
For example, an increase in the number of households will increase the demand for electricity connections. There has been a general trend in South Africa of a decrease in the number of people living per household (SACN, 2004). Therefore, there has been an increase in demand for formal housing in Durban from 1996-2004.
Technological change
Taking into account this growth, the municipality plans to provide everyone with official housing by 2017. The South African Breweries (SAB) plant is also committed to reducing its greenhouse gas emissions and aims to reduce emissions by 15% by 2015. SAB aims to achieve this through energy efficiency initiatives and switching to cleaner and renewable fuels by improving operational technologies (SAB, 2006).
Summary and Conclusions
Introduction
Scenario-based approach
- Definition of the project scope and level of detail required
- Identification of data required and compilation of a database
- Input of data into the model
- Creation of scenarios
- Comparison, evaluation and analysis of scenarios
75% of the total LPG is consumed in the industrial and commercial sector and 25% in the residential sector. As in the Low Carbon City, in 2050 the trade and service sector will represent 95% of the economy. The resulting job losses widen the gap between the rich and the poor in the city.
The downscaling approach
- Data sources
- Population
- Gross value added
- GHG emissions
After developing country-level data based on the above methodology, van Vuuren et al. 2007) state that the data can be downscaled from the national level to the grid level (0.5° x 0.5°) using a linear downscaling approach. Country-level data based on a survey by van Vuuren et al. (2007), were published on the website of the Netherlands Environmental Assessment Agency and were used to downscale South African population, GDP and emissions data to the Durban level. Therefore, van Vuuren et al. 2007) reduced South Africa's GHG emissions from the Africa and Latin America region to the Middle East (ALM) using the IPAT framework.
Conclusion
Greenhouse gas emissions are a function of socio-economic drivers such as population, economic growth and technological change, as discussed in Chapter 2. determined by economic growth, population and technology. However, a linear downscale has been chosen to estimate Durban's GHG emissions so that it follows patterns of reduced population and economic growth data. South African downscaled data were used to determine changes in greenhouse gas emissions in Durban.
Introduction
Growth without Constraints Scenario
The reason for the increase in emissions in the residential sector is the decline in the number of people per household, which increases the total energy consumption (Winkler, 2009) and the result of the transition of all households to electricity, which has higher emissions. factor than other energy sources such as paraffin, LPG and biomass. The higher emissions in the GWC scenario for Durban are due to the city having a higher rate of economic and population growth compared to the national GWC scenario. Emissions increase by a factor of 2.8 under the IPCC A1C scenario and about 1.7 times the base year level by 2030 under the IEA and EIA scenarios.
Business as Usual Scenario
This is explained by the increase in energy efficiency and the increase in the use of SWH. The main difference between the energy sources/fuel type in the GWC and BAU scenarios is the increase in the use of renewable energy in the BAU scenario. This is 5% more than in the baseline year, when the only significant amount of renewable energy used in the city was biomass, which accounted for 4% of the total energy used (Figure 5.6).
Natural Transition City
This is due to the changing structure of the economy and the increase in demand for energy in the transport sector, mainly due to urban sprawl and also the increase in sea and air transport. The dominant energy sources in the natural transition city are petroleum products, which provide 76% of total energy by 2050. Emissions from the commercial sector increase 5 times from the base year, as a result of rapid growth in the service sectors, ICT and tourism. .
Slow Go City Scenario
The main difference in the sectoral contribution to emissions is that there is a slight decrease in the percentage contribution to CO2e emissions from the transport sector, from 25% to 20%. This is due to the increased energy efficiency and use of public transport in the transport sector as fuel prices rise. The percentage contribution to CO2e emissions in the industrial and commercial sector increases from 52% to 55%, due to high economic growth rates and an initial slower response to climate change mitigation.
Low Carbon City
Many developed cities and some developing cities have started implementing climate action plans, which set reduction targets to become low-carbon cities. New York City's reduction targets are well above the reductions in the Low Carbon City, while the reductions for Bangkok, which is considered a developing city, are closer to the Low Carbon City. A major difference between other cities' climate action plans and Durban's Low Carbon City Scenario is the large reductions in emissions resulting from improvements in energy supply (Bloomberg, 2007, Mayor of London, 2007 and Tokyo Metropolitan Government, 2007).
Comparison of scenarios
- GDP, income per capita and CO 2 e emissions
- Population and households
- Fuel mix
- Energy and carbon intensity
- Structural change
- Summary of drivers and emissions
Solid fuels increase in GWC, BAU and Slow Go City, but decrease for the Natural Transition and Low Carbon City Scenarios. The percentage contribution of natural gas increases from 2% of total energy demand in the base year to 6% in 2050 for Slow Go City. CO2e emissions per capita are expected to increase by up to 3.2 times from the base year in the GWC Scenario.
Downscaled scenarios
- A1 Scenario
- A2 Scenario
- B1 and B2 Scenarios
- Conclusion
The main difference in the storylines is that the GWC scenario has high population growth, while in the A1F scenario the population decreases. Emissions for the Natural Transition Scenario are lower than the A2 storyline due to the structural change in the economy. Furthermore, emissions for the GWC scenario are higher than for the A1F scenario because the GWC scenario assumes that there are no technological changes in the economy that would improve efficiency.
Introduction
Summary of results
The creation of scenarios
It has been determined that the City has set an economic growth rate of 1% above the National target. In terms of GHG mitigation, the City plans to reduce emissions by 27.6% of the GWC level. The Natural Transition City accepts that municipal involvement in the city is reduced and that the economy is left to free markets.
The scenarios
The purpose of this scenario is to illustrate the implications on emissions if the city is slow to respond to climate change, but very efficient in the future, due to energy price increases and climate change disasters. Emissions increase 2.5 times from the base year, with a rapid increase in emissions until 2015, followed by a decrease in the growth of emissions. The rapid response to mitigating climate change in the Low Carbon City resulted in a 15% reduction in emissions by 2025 from the base year.
Recommendations and way forward
Recommendations for achieving a low carbon future
However, as the economy grows, energy demand outpaces mitigation measures, resulting in a 1% reduction in emissions from 2005 to 2050. It is therefore vital that this sector targets future emission reductions. While the residential sector contributes minimally to emissions today, emissions from this sector could potentially increase as people become wealthier and the population grows.
Recommendations for further studies
Integrated Transport Plan eThekwini Transport Authority, Durban, http://www.durban.gov.za/durban/services/eta/policy, Accessed 4 March 2009. World Energy Outlook 2008, International Energy Agency, France, http:/ /www .worldenergyoutlook.org/docs/weo2008/WEO2008.pdf, Accessed June 23, 2009. World Energy Outlook 2009 Climate Change Excerpt [online], International Energy Agency, France, http://www.worldenergyoutlook.org/2009.asp , Accessed 23 June 2009.
Background Document: A Catalyst for Accelerate and shared growth South Africa, Summary, http://www.info.gov.za/speeches/briefings/asgibackground.pdf, accessed 11 August 2009. UK Department of Trade and Industry (2003 ), Energy White Paper Our Energy Future - Creating a Low Carbon Economy, The Stationery Office (TSO), London, UK, www.dti.gov.uk/files/file10719.pdf, accessed 2 October 2010.
