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The Chinese application of IAMs, reviewed previously, all share similar shortcomings.

All models are simplified representations of reality. Because the climate models need data support and parameter estimation, there are several criticisms of the modelling framework. This section discusses the problems with Chinese IAMs and possible solutions. WITCH offers solutions to several IAM criticisms. These solutions are simplification, discount rates, comprehensive input data requirements, static modelling framework, no optimal generation scenarios, climate damage functions, emissions economy linkage, economic growth, regional aggregation, regional interaction, technological innovation and economic response to policy (Table 4.6).

Appendix 13 contains the full summary of WITCH’s solutions to common IAM criticisms.

Table 4.6: WITCH’s solutions to common IAM criticisms Category Criticism

General Simplification

Discount Rates Model Flexibility

Comprehensive Input Data Requirements Static Modelling Framework

No Optimal Generation Scenarios Climate Change Climate Damage Functions

Emissions Economy Linkage Energy Demand

Economic Growth Regional Aggregation Regional Interaction

Energy Supply Technological Innovation and Transfers Policy Impact Economic Response to Policy

Simplification

The purpose of an integrated assessment model is to aggregate diverse strands of disciplines. However, this creates complexity which may render an IAM less accurate.

This computational complexity puts a hard limit to the amount of detail that can be represented in the model. The trade-off is made between complex realism and stylised simplicity. The model outputs may not be defensible. The result from the limited scenario runs is that future assessments may lean too heavily on downscaled scenarios.

The output is too coarse for regional assessment, and the precision of downscaling is determined by the model output that may be highly inaccurate. More importantly, downscaled scenarios only provide one future climate state, usually the mean. Hence, they are less adequate at producing the extremes around that mean. Yet it is quite clear that on a regional basis, extreme events will have the greatest impacts on regions.

The possible solution for over simplification is to use a hybrid methodology. The WITCH hybrid methodology takes both a top-down view (welfare maximisation) in addition to a bottom-up view (energy generation cost minimisation). Models of complex socio-economic systems require the simplification of assumptions regarding energy systems and climate relationships. There are two broad approaches for modelling the interaction between energy, the environment and the economy. Their main difference is with respect to the emphasis placed on a detailed and technologically based treatment of the energy system, and a theoretically consistent description of the general economy.

Top down models are general economic models with only a rudimentary treatment of the energy system. Using the top-down approach, they describe the energy system (similar to the other sectors) in a highly aggregated way by means of neoclassical production functions that capture substitution possibilities through substitution elasticities. Technological change only alters the costs of production at a commodity or industry level. Macro-econometric models offer a lot of economic detail, but little energy technology detail. However, this methodology does not provide the degree of disaggregation for detailed energy generation technology analysis. This severely constraints the analysis policies in the face of climate change, as climate change is closely related to the evolution of energy sector technologies. They usually do not rely on detailed and direct descriptions of technologies. Bottom-up modelling is based on

linear activity models with a large number of energy technologies to capture the substitution of energy carriers on the primary and final energy level, process substitution, process improvements (gross efficiency improvement, emission reduction), or energy savings. They are mostly used to compute the least-cost method towards meeting a given final energy demand subject to exogenous constraints of emission reduction targets. Bottom-up models embed new technologies and model the penetration of these technologies based on costs and performance characteristics.

Technological change occurs as one technology is substituted by another. However, bottom-up models are not well suited to studying strategic considerations amongst macro regions (Loschel, 2002). A solution to this shortcoming can be realised through a hybrid methodology.

Discount Rates

Climate modelling requires a long-term looking-forward time window. However, the more weight that is placed on the future, the more optimal climate change control rates rise. The discount rate is critical since small changes in the yearly discount rate compound over many years and decades. If a low rate of time discount is used in models which allow for economic responses to policies, investment rates are higher and hence growth is higher. Hence uncontrolled emissions are higher, which further increases the optimal control rates.

Although WITCH does not endogenously deal with issues of discount rates, a solution of uncertainty in the appropriate discount rate is to perform a sensitivity analysis on a range of rates that WITCH can accommodate. The breadth will be determined by a survey of discount rates used in existing climate modelling and IAM studies. To supplement the survey of the discount rates, support can also be drawn from the IEA, World Bank and the UN. These governmental agencies all have differing views on the discount rates that should be applied to different global regions.

Input Data Requirements

Data input is normally less problematic at China’s national macroeconomic level.

However, data availability can be problematic at provincial microeconomic levels as can gathering information on the detailed energy sector. Problems of input data requirements are especially an issue for Simulation and input-output analysis. They often require highly detailed energy sector data to function. A trade-off is made

between the recentness of the data against its completeness. To have a more comprehensive overview of the energy sector, YE uses outdated input-output tables from 1987 and Liang uses tables from 1997. Additionally, comparing YE against other models, its dynamic input-output framework is not suited to long-run energy policy analysis. Since the input-output table projects are inaccurate in the very long run, YE’s model results are also inaccurate in the long-term (Teng et al., 2007).

A possible solution to the lack of comprehensive input-data requirement is to reduce energy systems into stylised representations. Using nested production functions of WITCH, the energy sector can still be captured and modelled. However, any further stylisation will inevitably lead to inaccurate modelling. Hence a balance has to be struck between detailed modelling (albeit computationally difficult) and stylised representations (potentially inaccurate). Issues of highly detailed data inputs can be overcome via WITCH, as it does not have as a detailed representation of the energy sector as the pure Cost Minimisation models, but takes a more detailed perspective than pure growth maximisation models. Hence WITCH is the ideal trade-off between reducing data input requirements via stylisation and having a detailed enough representation of the energy sector to aid energy policy makers in their decision making process.

Static Modelling Framework

Static modelling does not account for subsequent changes in the climate, energy systems and the economy. The limitation of YE and Liang lies in its static analytical framework. For example, Liang assumes a temporal stability for direct model inputs.

This assumption is suitable for developed countries, but less suitable for developing nations. The rapid change of developing nations, such as China, calls for a dynamic modelling framework. A behaviour-oriented model dynamic framework would enable energy policymakers to understand the interaction between within the economic system better (Teng et al., 2007).

A possible solution to the static modelling framework is to use an iterative simulation process via a Nash game. At each new iteration, energy policy makers take the behaviour of other energy policy makers into account. This newly computed output is carried over to the following period as an input in the next round of optimisation. The process is iterated until each Chinese province’s behaviour converges as the best

response against other regions. WITCH is a dynamic model that iterates in regular intervals under a game theoretic framework. Each period’s outputs are fed into back into the model as an input. For inputs which are dependent on previous period, researchers do not have to exogenously set the levels each period. WITCH automatically calculates inputs such as energy supply and demand for the next period endogenously.

No Optimal Generation Scenarios

To aid the setting of energy targets, energy policy makers often prefer to project optimal generation scenarios and pathways. This preference can cause problem for Cost Minimisation models as this class of models does not propose optimal energy generation pathways. They merely project the least cost pathway, which could be suboptimal if energy policy makers also need to account for SoS issues. In other words, the least cost pathway is not necessarily the best. For example LEAP does neither generate optimised scenarios, nor produce market-equilibrium scenarios. It can only identify the least-cost scenario. Furthermore, LEAP does not estimate the impact of energy policies on GDP growth or job creation. Additionally, 3E is mainly driven by the ESOM module. ESOM is a multi-period linear program model based on the energy flow system. The module output are activity levels of kinds of technologies, energy consumptions of end use energy technology, intermediate transfer technology, pollution emissions and technology investment and cost. However these model outputs are according to the generation technology and not end-use sectors. Hence the model can only offer energy policy guidance on generation supply and not offer guidance on consumption demand. Since energy consumption derives the level of supply generation (as heat and electricity are difficult to store and have to be generation on demand) the model ignores a vital area of the energy sector (Teng et al., 2007).

Climate modelling can be for different purposes. One of the most common purposes of climate modelling is to propose energy mix targets via projecting generation pathways. Chinese energy policy makers are not only concerned about emissions abatement. WITCH allows energy policy makers to balance emissions abatement and GDP growth targets. Thus it is an important policy action in the arsenal for China’s continued fight to reduce emissions whist pursuing economic growth.

Climate Damage Functions

Most of the Chinese IAM analysis takes the view that although carbon emissions are of concern, the research does not aim to calculate hard emissions or atmospheric ppm caps that restrict economic growth. The Chinese research is more focussed on gradual ways to reduce emissions in a way that does not radically adversely impact GDP growth. In the cost minimisation and input-output simulation models, researchers are not concerned with the optimal mix of energy generation supply and the mix of energy consumption sector demand. In general equilibrium analysis, researchers are primarily concerned with the price level to achieve the desired level of GDP output that the state has targeted and less focussed on include the intangible cost of emissions into the calculated price levels.

The modelling of economic growth, emissions and temperature change has been at the forefront of debate on IAM analysis. Finding a solution to the correct climate damage function is worthy of being separated into a separate research project. A compromise is to use the consensus data for growth, emissions and temperature change from the Working Group III of the IPCC. In the same spirit, the WITCH runs various emissions scenarios as a function of temperature increases. The model takes temperature changes as exogenous inputs and dispenses with the added complexities of modelling energy-climate relationships. The emissions as a function of temperature increase draw on IPCC data. The IPCC has surveyed a range of temperature rises relative to economic activity. A 5% confidence interval either side of the mean would lead to more robust results.

Emissions Economy Linkage

Problems of lack of feedback between the macroeconomy and climate change relates to a disjointed modelling framework between economic growth and GHG emissions.

This is especially problematic for Cost Minimisation models such AIM and MARKAL, which focus less on the macroeconomy. For example in AIM, there is a lack of feedback between the macroeconomic module and the climate module. As a result, indicators for economic losses including externalities causing a reduction to GDP are excluded. Additionally, IAM only consider existing generation technologies.

Backup technologies are not included as alternatives therefore the CO2 emission reduction will be underestimated. Additionally, MARKAL does not model the relationship between the energy subsector and the general macro-economy. The

MARKAL Model is usually applied independently as is the economic growth, thus energy demand is given exogenously. The improvement made to MARKAL is the hard linked MARKAL-MACRO. This model is a dynamic nonlinear program, with an objective function that seeks to optimise total discounted utility. It incorporates a macroeconomic module depicted as a production function of constant elasticity of substation. The limitation of MARKAL-MACRO is its simplified expression of the macroeconomy. Different generation technologies and end-use sectors respond differently to price changes, with the responsiveness also altering over time. The model can only roughly capture the changes in energy demand resulting from changes in economic structure (Goldstein and Greening, 2001).

WITCH is a hard-linked model because the pollution of the energy module is fully integrated with the rest of the economy. Linkage between growth, emissions and environmental damage is provided via the climate module. A climate module and a damage function provide feedback on the economy regarding carbon dioxide emissions into the atmosphere. The energy component of the aggregate production function has been expanded to depict the energy sector and to model the carbon mitigation options for the main greenhouse gases. The model tracks actions which impact the level of mitigation, such as R&D expenditure, investment in carbon-free technologies and adaptation, purchases of emission permits, or expenditure on carbon taxes. The hard-link nature of WITCH allows emissions or temperature increases to be fully reflected in a climate-economic framework. The policy maker can decide to fix a target in terms of maximum tolerable damage or in terms of climate damage reduction that needs to be accomplished and the optimal portfolio of strategies can be determined accordingly.

Economic Growth

Most climate modelling tie GHG emissions to industrial production either implicitly through emissions projections, which are based on the growth in industrial production or explicitly through an actual model of industrial production. Economic growth results directly from advances in technology and population growth, and from a high marginal product of capital in the developing world. If population growth were curtailed technological advances restricted, economic growth will also reduce, following the Cobb-Douglas production function as introduced in Section 1.1. With lower economic growth, there is less output and less associated pollution.

A solution to uncertainly in future economic growth rates is endogenous variables which are uncertain (such as capital and energy use) and exogenous variables which are published (namely labour). WITCH uses a Cobb-Douglas production function based on labour growth rates from the World Bank. Coupled with the calculation for capital via a Nash-Iterative game that incorporates investment and depreciation rates, the WITCH model captures economic growth as well as any other welfare maximising IAMs.

Regional Aggregation

Many IAM methodologies are globally integrated and do not consider different countries or regions. Globally integrated optimisation models do not account for differences in endowment of capital and labour. They also do not capture the degree of economic growth and the associated energy needs.

The ideal solution to fully reflect regional disparities would be to model each country separately. However, this would result in a computationally heavy framework. The compromise that researchers have employed is to group regions with similar levels of energy demand (economic growth) and supply (natural resource endowment). Since this research focuses on Chinese energy policy, China is better advised to be a separate region. In WITCH, the world is grouped into 12 regions. From the 12 regions, there are two which consist of one country, namely CHINA and USA. In the 10 remaining regions, seven countries are clustered together based on geographical proximity as are three groups of countries based on similarities in economic growth.

The seven regions which cluster countries based on geographical proximity are OLDEURO, NEWEURO, MENA, LACA, SSA, SASIA and EASIA. The three regions clustered based on level of economic growth are CAJANZ, KOSAU and TE.

This classification of countries is partially based on the UN’s population data (UN, 2010). OLDEURO represents Western Europe, or the developed European countries which are able to accede into the European Union. NEWEURO are the Eastern European countries which aspire to increase levels of GDP per capita and limit inflationary pressures, in order to become eligible for EU accession. MENA represents the Middle Eastern and North African regions. This region has vast reserves of petroleum and natural gas reserves, making their cost of energy generation cheaper than areas without high natural resources abundance. The LACA region is

represented by Latin America, Mexico and Caribbean. There areas are culturally very similar and benefit from similar endowment of renewable energy resources. The SSA is represented by Sub-Sahara and Africa, but excludes South Africa. This mineral and resources rich region has low capital endowment. SASIA covers the South Asia region, whose energy demand and supply are dominated by India, Bangladesh, Pakistan and Sri Lanka. EASIA or South East Asia includes Brunei, Burma, Indonesia, Malaysia, Philippines, Singapore, Thailand and Vietnam. The CAJANZ includes Canada, Japan and New Zealand which have similar GDP per capita of around USD 35,000 for 2010 (World Bank, 2011). KOSAU consists of Korea, South Africa and Australia which are similar in their wealth of natural resources reserves endowment.

Transition Economies (TE), mainly located in the former USSR, act as the balancer in order to obtain the aggregate global GDP.

Regional Interaction

Each nation of region will behave strategically relative to policy decisions from other nations. Hence projecting future policy outcomes are path dependent. In other words, nations will not behave statically.

In WITCH, strategic interaction between regions is represented through an open-loop Nash game. Investment strategies are optimised across each region, by taking into account both economic and environmental externalities. Across time, WITCH is a dynamic model whereby each time step looks forward for stakeholders to maximise welfare. Welfare is maximised strategically with respect to anticipated policy choices by other decision makers, in a game theoretic manner. The investment strategies are optimised by taking into account both economic and environmental externalities. The model seeks to find the optimal investment profile for each region and technology via an inter-temporal game.

Technological Innovation and Transfers

Technological improvement which in turn lowers energy generation costs is modelled through learning by doing and technological disbursements. For example, in the Chinese General Equilibrium models, they lack detail on the impact of technological change in reducing generation costs. Technological improvements lower generation costs, making sustainable technologies more competitive relative to its fossil-fuel competitors (Teng et al., 2007).

There are two ways in which technological improvements can reduce emissions. The

There are two ways in which technological improvements can reduce emissions. The