The impact of modified EU ETS allocation principles on the economics of CHP-based district heating systems

Journal of Cleaner Production 20 (2012) 47e60

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The impact of modified EU ETS allocation principles on the economics of CHP-based district heating systems Günther Westner a, Reinhard Madlener b, * a

E.ON Energy Projects GmbH,1 Arnulfstrasse 56, 80335 Munich, Germany Institute for Future Energy Consumer Needs and Behavior (FCN), School of Business and Economics/E.ON Energy Research Center, RWTH Aachen University, Mathieustrasse 6, 52074 Aachen, Germany b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 23 February 2011 Received in revised form 25 July 2011 Accepted 1 August 2011 Available online 6 August 2011

The economics of large-scale combined heat and power (CHP) generation for district heating (DH) applications are strongly affected by the costs and allocation mechanism of CO2 emission allowances. In the next period of the European emissions trading system (EU ETS), from 2013 onwards, the allocation rules for CHP generation will be modified according to the principles announced in EU Directive 2009/29/ EC. In this paper, we analyze the impact of the intended modifications on large-scale coal- and gas-fired CHP plants for DH in Germany. By means of a discounted cash-flow model we first show that the implementation of the modified allocation mechanism significantly reduces the expected net present value of the technologies considered. In a next step, by applying a spread-based real options model, we analyze the decision-making problem of an investor who intends to invest in CHP generation. Our results provide some evidence that the modified EU ETS principles contribute to reducing the attractiveness of investments in large-scale CHP plants that feed into DH systems. If these effects are not compensated, this could lead to a situation where highly efficient CHP plants are gradually replaced by separate power and heat generation in boilers respectively fossil-fueled condensing plants. Ó 2011 Elsevier Ltd. All rights reserved.

Keywords: Combined heat & power Emissions trading system Investment under uncertainty Spread Real options

1. Introduction The EU climate and energy package, released in January 2008, sets three ambitious targets for the year 2020: the cut in greenhouse gas (GHG) emissions by at least 20% relative to 1990 levels (30% if other developed countries commit to comparable cuts), the increase of the share of renewable energy sources (wind, solar, biomass, etc.) to 20% of total energy consumption, and the cut in total energy consumption by 20% of projected 2020 levels through improved energy efficiency. In order to realize the considerable reduction of GHG emissions by 2020, the EU Commission aims at increasing the costs of GHG emissions for sectors that are subject to the EU Emissions Trading System (EU ETS) as well as for nonETS sectors. Emissions in non-ETS sectors can be regulated via fuel taxation or introduction of specific CO2 taxes, whereas the ETS

* Corresponding author. Tel.: þ49 241 80 49 820; fax: þ49 241 80 49 829. E-mail addresses: [email protected] (G. Westner), RMadlener@eonerc. rwth-aachen.de (R. Madlener). 1 Please note that all statements in this article are made by the authors only and do not necessarily reflect the views of E.ON Energy Projects GmbH. 0959-6526/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.jclepro.2011.08.001

sectors are guided by the revised general principles of the EU ETS. The new ETS Directive 2009/29/EC of the European Union, released in April 2009, determines the new framework of the allocation mechanism for CO2 emission allowances in the third ETS period from 2013 to 2020. The directive also modifies the allocation rules for combined heat and power (CHP) generation and harmonizes them across all EU member states. The new rules imply substantial changes concerning the allocation of free emission allowances to CHP generation. From 2013 onwards, such installations receive only an allocation for the produced heat and no longer for the electricity production. This has major impacts on the economics of CHP systems. In our research we investigate how the economics of CHP installations for district heating (DH) are affected by the modified principles as described in the EU ETS Directive. District heating, defined as space and water heating of several buildings or larger areas by means of centralized generation units, can significantly contribute to achieving emission reductions. In our study we explore the likely consequences of the intended adjustments of the EU ETS in the context of CHP-based DH in Germany. We chose Germany for our investigation as it represents the largest European energy market and, according to the national potential study (Eikmeier et al., 2005), has a large

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economic potential for CHP-based DH installations of up to 240 TWhel per year (based on 2010 figures this represents a share of 39% of total power generation in Germany). Only about 20% of this potential is utilized today. The realization of the remaining potential depends, among other things, crucially on the future design of the EU ETS. This paper is structured as follows. Section 2 describes the benefits of CHP-based DH. Section 3 provides a concise overview of the existing European emissions trading system and the modified rules for CHP generation in the next EU ETS period. The input parameters and results of the financial model are described in Section 4. Section 5 applies a spread-based real options model to evaluate the impact of the modified EU ETS principles on future investment decisions. Section 6 concludes. 2. CHP-based district heating systems In this section we introduce a possible classification of CHPbased DH systems and describe the benefits of CHP generation concerning primary energy savings and reductions in CO2 emissions. 2.1. Classification of district heating systems DH systems primarily focus on supplying low- and mediumtemperature heat demands for space heating and hot water production. It is a typical characteristic of DH systems that the heat is generated centrally and then distributed via a well-insulated network of pipes to the locations where it is utilized for residential, public, or commercial heating requirements. In principle, various heat sources, such as heat from CHP generation, heat boilers or different forms of renewable heat sources (e.g. geothermal energy), can be used to feed DH systems. The decision about which heat source is chosen depends on the timing and nature of the thermal load, fuel availability, and the economic utilization of the electricity that is in many cases produced in a combined process. Even “waste” heat streams that are difficult to utilize otherwise can be implemented. Over time, the heat input of DH systems can gradually be converted into renewable heat sources as new technologies become available and cost-effective. In this way, DH systems create a bridge towards future low-carbon energy supply systems. Population density is a key consideration for new DH systems, as these rely on a concentrated demand for space heating to minimize heat transportation distances and losses and thus mitigate operating costs. Development and construction of new DH systems require high investment costs, but help to provide a long-term asset with the perspective of a transition towards a low-carbon energy system. The huge variety of different applications of DH and vague distinguishing criteria make it hard to find a proper classification for DH systems. One possible approach is a classification into local heat systems and large-scale urban DH systems, according to

structure, size, and the kind of heat sources or heat sinks connected (Grohnheit and Mortensen, 2003). Local heat systems are DH networks of comparably small extension in villages or insulated urban districts. In many cases they are fed by few small-scale CHP units. Large urban DH networks are interconnected grids of a wider extension. They are supplied by a number of different energy sources, such as coal, oil, natural gas, solar power, geothermal heat, waste, or surplus heat from industrial production. The heat production units usually apply various generation technologies to produce the heat required. A main purpose of urban DH networks is to connect the different kinds of heat sources. The baseload of the heat demand is in many cases delivered by large-scale coal- or gas-fired CHP plants. A more detailed analysis of local systems and urban DH networks can be found in Lazzarin and Noro (2005). Table 1 provides an overview of the typical characteristics of local heat systems in comparison with urban DH networks. 2.2. Primary energy savings and emission reduction through CHPbased district heating The heat production of CHP plants can be utilized as thermal input for DH systems. The selection of the CHP plant depends on the specific requirements of the DH system. In general there are various CHP installations available, which differ in the applied technology, the size of the plant, the fuel type, or the mode of operation (Westner and Madlener, 2011a). CHP technologies bear a substantial potential to increase energy efficiency and reduce CO2 emissions compared to separate power and heat production (Korhonen, 2002). The IEA highlights the key role of CHP generation when it comes to CO2 emissions reductions, and states that CHP provides a meaningful contribution to achieving significant greenhouse gas emissions reductions that are necessary to avoid major climate change and resulting disruptions (IEA, 2008a). The evaluation of primary energy savings and CO2 emissions reductions through CHP generation depends significantly on the chosen reference technologies (Verbruggen et al., 1992). In this section, we illustrate primary energy savings and CO2 emissions reductions of selected CHP technologies that we later use for the model-based evaluation of the new EU ETS principles. We consider large-scale technologies that are applied in urban DH networks as well as small-scale technologies for local heat systems. 2.2.1. Large-scale CHP technologies In our investigation, we focus on two commercially available and approved kinds of large-scale CHP technologies: coal-fired steam turbine plants and combined-cycle gas turbine (CCGT) plants. We take conventional heat boilers and condensing plants of the same technology as reference technologies for the evaluation of possible savings. In contrast to CHP plants, condensation plants do not utilize the heat occurring in the process of power

Table 1 Classification of CHP-based DH networks.

Extension Heat load [GWh] CHP-based heat sources

Type Size Number Fuel

Local heat systems

Urban DH networks

Length of pipelines 10 km <100 GWhth/a Small-scale CHP plants (e.g. engine CHP, micro-turbine, Stirling engine, fuel cell) 1 MWele100 MWel 1 to max. 10 small-scale plants In many cases natural gas

Length of pipelines 10 km >100 GWhth/a Large-scale CHP plants (e.g. coal-fired CHP, CCGT-CHP)

Source: Own illustration, with input from Fischedick et al. (2006).

100 MWele400 MWel Several plants of different size and generation technology Several fuel types are possible (hard coal, lignite, natural gas)

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generation. The average annual fuel utilization of coal-fired CHP plants lies in a range between 50% and 80%, depending on the degree of heat utilization at the respective site. The operation mode of CHP plants is usually determined by the heat demand. The relation between power and heat output, the so-called powerto-heat ratio, is for coal-fired CHP plants significantly lower in comparison to CCGT-CHP technology. Consequently, the heat output of an 800 MW coal-fired CHP plant is comparably high and, therefore, it is often not possible to utilize the total amount of heat at the site. For this reason, coal-fired CHP plants are often operated partially in CHP mode. For CCGT-CHP, which is characterized by higher power-to-heat ratios, the situation is different and, therefore, they are usually operated in full CHP mode. In our investigation, we refer to the most common applications of CHP plants and model the coal-fired CHP plant only partly in CHP mode and the CCGT-CHP plant in full CHP mode. As shown in Fig. 1, the realized primary energy savings depend on the heat utilization and lie for a coal-fired CHP plant in a range between 5% and 25%, whereas the emission reduction is in a range between 1% and 10%,

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in comparison to the separate generation of power and heat. A gas-fired CCGT-CHP plant reaches a higher average fuel utilization of about 80%. Based on our assumptions, the CCGT-CHP plant can reach primary energy savings between 9% and 20% and CO2 emissions reductions in the same range. The gas-fired CCGT-CHP, in comparison to the carbon-intensive coal technology, reaches significantly higher emissions reductions. 2.2.2. Small-scale CHP technologies In a next step, we evaluate primary energy savings and CO2 emissions reductions of small-scale CHP applications. Exemplarily, we consider engine CHP and micro-turbine CHP. Usually, it is not meaningful to install these technologies without heat utilization and, therefore, we take the German power mix as a reference value to evaluate the electrical output of the units. In 2009 the average efficiency of power production in Germany was 37%, with an average CO2 emission factor of 575 gco2 =kWhel (Umweltbundesamt, 2010). In many cases CHP plants replace old and inefficient plants with even higher specific CO2 emissions.

Fig. 1. Primary energy savings and CO2 emission savings of large-scale CHP plants in comparison to condensing plants and heat production in gas-fired boilers. Source: Own illustration.

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The impact on primary energy savings and CO2 emissions reductions is then even higher than the average values. According to Fig. 2, the primary energy savings of engine CHP compared to power supply from the German grid and heat production in gasfired heat boilers ranges between 31% and 38%. The primary energy savings of micro-turbine CHP is 10%e33% and thus lower due to the less favorable power-to-heat ratio. This is also the reason why the CO2 emissions reductions for engine CHP (34e41%) are higher compared to those of micro-turbines (14e37%). 3. The European emissions trading system The intention of the EU ETS is to support the member states in reaching the agreed greenhouse gas (GHG) emissions targets in a cost-efficient manner. Currently, the EU ETS covers more than 11,500 energy-intensive facilities in 30 countries.2 These facilities include oil refineries, power plants with more than 20 MW in thermal capacity, coke ovens, iron and steel plants, as well as cement, glass, lime, brick, ceramics, and pulp and paper installations (CEC, 2005). The entities covered emit about 40e45% of the EU’s total GHG emissions. Up to now, the EU ETS does not cover the CO2 emissions of the transportation sector, which account for about 25% of the EU’s total GHG emissions, and emissions of non-CO2 greenhouse gases, which account for about 20% of the EU’s total GHG emissions (EEA, 2009). The implementation of the EU ETS takes place in phases, with periodic reviews and opportunities for expansion to further greenhouse gases and sectors. The current, second ETS period began on January 1, 2008 and covers the first commitment period of the Kyoto Protocol until the end of 2012. The upcoming phase three will begin in 2013 and will last for eight years until the end of 2020. In this section, we describe the most important changes in the next ETS trading period from 2013 onwards and discuss the treatment of CHP installations for DH within the modified EU ETS principles. 3.1. General principles in the third EU ETS period The ETS Directive 2009/29/EC (CEC, 2009b) of the European Union, released in April 2009, defines the principles for the allocation of CO2 emission allowances in the next EU ETS period from 2013 onwards. Since its release, the Directive 2009/29/EC has been supplemented by two further EU Commission decisions that define the sectors which are deemed to be exposed to a significant risk of carbon leakage (CEC, 2009a) and determine Union-wide rules for the harmonized free allocation of emission allowances (CEC, 2010). The new principles promote the harmonization of the existing rules within the community and differ in some points significantly from the allocation principles of previous EU ETS periods. According to the directive, the free allocation in the third EU ETS period is based on uniform product-based benchmarks that are aligned to historic activity levels. The definition of the benchmarks is based on the average performance of the EU-wide 10% most efficient installations of their kind in the years 2007e2008. The historical activity levels that are multiplied by the product benchmarks are based on the median production during the period from January 1, 2005 to December 31, 2008, or, where it is higher, on the median production during the period from January 1, 2009 to December 31, 2010. The activity levels for new installations that enter the market are based on standard or installation-specific capacity

2

The 27 EU Member States plus Iceland, Liechtenstein, and Norway.

utilization. Carbon leakage sectors (energy-intensive sectors that are subject to international competition and face major economic disadvantages through the EU ETS) receive a 100% free allocation of the value calculated based on product benchmark and historical activity levels. For the rest of the affected industries (noncarbon leakage industries) the percentage rate for free allocation decreases from 80% of the calculated value in 2013 to 30% in 2020. In the case that European Union-wide reduction targets, as defined in Directive 2009/29/EC, are not realized during the period, the quantity of freely allocated allowances can be additionally decreased by application of cross-sectoral correction factors. For the whole power sector, full auctioning will be introduced in the next ETS period and new plants for production of electricity will not receive a free allocation. The commercial aviation sector will also be included in the EU ETS from 2013 onwards and more consistent and harmonized monitoring, reporting, and verification requirements will be introduced. Table 2 contains a comparison of allocation methodologies between the ETS trading periods. 3.2. Modification of allocation rules for CHP-based DH plants in the third ETS period CHP installations for DH applications with a thermal capacity above 20 MW are implemented in the EU ETS. In the current, second ETS period, the principles for the allocation of free emission allowances in the power sector in general, and for CHP installations in particular, are not harmonized and differ significantly between the EU member states as well as between existing assets and new plants (Rogge and Linden, 2008). Many countries (e.g. France, UK, Spain, and many Central and Eastern European countries) allocate allowances for CHP plants according to their historic emissions (grandfathering). Other member states, e.g. Austria, Denmark, or Ireland, use a uniform benchmark to allocate allowances. In a third group of countries, including Germany, Italy, and the Netherlands, the allocation for CHP installations is based on the double benchmark principle. In our investigation, we take a closer look on the double benchmark principle, which is implemented in the German NAP of the second ETS period. We use the double benchmark also as a reference for the later investigation and compare the allocation mechanism of the upcoming third EU ETS period with this principle. According to the double benchmark principle, CHP plants receive an allocation based on the produced power and an additional allocation based on the produced heat. The allocation for the electrical output refers to the emissions of a conventional fossil-fired power plant, whereas the allocation for the heat output refers to the emissions of a conventional boiler or steam plant. According to the German NAP, the free allocation for CHP plants is calculated by applying the following formula:

ADB ¼ VA $BMA þ VQ $BMQ :

(1)

Equation (1) states that the amount of allocated emission allowances (ADB) for a full year in the second ETS period depends on the power production (VA) and on the heat production (VQ) of the CHP plant, weighted by the respective benchmark factors for power (BMA) and heat (BMQ). The production volumes VA and VQ do not represent the actual production of power and heat in the respective year. They are calculated based on the installed capacity and on installation-specific capacity utilization factors. The capacity utilization for CHP plants is, e.g., 7500 hours per year. Table 3 contains the benchmarks for power and heat production according to the German NAP (BMU, 2006).

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Fig. 2. Primary energy savings and CO2 emission savings of small-scale CHP plants in comparison to the German power mix and heat production in gas-fired boilers. Source: Own illustration.

In the third ETS period, highly efficient installations that produce heat and power in a combined process (as defined in Directive 2004/8/EG) only receive an allocation for the heat output according to a uniform product benchmark. For existing

installations, the amount of free allocation depends further on historical activity levels. The cross-sectoral correction factor that can be used to cut free allocation in the case that European Unionwide emission targets are not accomplished, is not applied to CHP

Table 2 Comparison of allocation methodology between the ETS trading periods.

Degree of harmonization Free allocation

Auctioning

ETS Periods I & II

ETS Period III

Allocation according to heterogeneous national allocation plans. By majority grandfathering, some countries (e.g. Germany, Poland, Denmark) apply benchmarking.

Harmonized allocation rules ensure consistency of scope and definitions; Greater EU central responsibility.  Limited free allocation according to product benchmarks and historical activity levels.  Differentiation between carbon leakage sector (100% free allocation) and other industries (decreasing free allocation from 80% in 2013 to 30% in 2020).  Application of a cross-sectoral correction factor in case the European Union-wide emission targets are not reached. 100% auctioning for the power sector; Increasing share of auctioning for non-carbon leakage industries.

Limited amount of allowances is free for auctioning (<5% in period I; <10% in period II).

Source: Own illustration, with input from IEA (2008b).

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utilization factors of new installations have still not been announced. The benchmark for heat is defined in the Commission’s decision (CEC, 2010) with 0.0623 allowances/GJ, which is equal to 0.224 allowances/MWh. A differentiation between solid and gaseous fuels is no longer intended. The linear reduction factor (LF) indicates that the free allocation of emission allowances will decrease linearly from 80% of the calculated value in 2013 to 30% in 2020. According to these principles, CHP installations receive significantly less allowances from 2013 onwards compared to the current situation in the second period. The example in Fig. 3 illustrates the difference between the allocation principles for a coal-fired CHP steam plant and a gas-fired CHP-CCGT plant. The transition from fuel-specific benchmarks, which are state-of-the-art in the second ETS period, to product-based benchmarks for heat production leads to a situation where both technologies receive the same amount of free allowances independently from the carbon intensity of the applied fuel.

Table 3 CHP benchmarks according to the German NAP 2008e2012. Product

Benchmark [gCO2 /kWh] (solid fuel/gaseous fuel)

Power Hot water Process steam

750/365 290/215 345/225

4. Economic evaluation of modified EU ETS allocation rules for CHP-based DH 4.1. Input parameters

Fig. 3. Comparison of the allocation mechanism for CHP plants with a heat capacity of 400 MWth in the second and third ETS allocation periods.

installations. Based on the currently available information, CHP installations for DH will receive from 2013 onwards an annual allocation according to the following formula:

A3rd Period ¼ HALQ $BMQ $LF:

(2)

Equation (2) shows that the amount of freely allocated emission allowances (A3rd Period) for a full year in the third allocation period depends on the historical activity level of heat production (HALQ) and on the product benchmark of heat (BMQ). For new installations, the historical activity level is replaced by a production capacity, which is calculated based on the total installed heat capacity and a defined standard utilization factor. The values for the standard

In this section, we introduce the main input parameters of our economic evaluation model: technical and operational parameters of the considered plants and historical commodity prices. We use these parameters to quantify the economic feasibility of different CHP applications for heat generation in the context of the modified EU ETS principles. 4.1.1. Technical and operational parameters In our investigation we consider four different kinds of commercially available CHP applications. The considered largescale plants, such as coal-fired CHP and CCGT-CHP, are typical units to cover the baseload demand of large urban DH networks. Small-scale plants, such as engine CHP and micro-turbine CHP, are typical applications that feed into local heat systems or supply isolated heat sinks, such as hospitals or public buildings. The largescale units are subject to the EU ETS, while the small-scale applications are not included. Table 4 contains the technical and operational assumptions made in our analysis. The electrical efficiency of CHP plants is lower compared to pure condensing plants of the

Table 4 Technical and operational input parameters for modeling CHP technologies.

Electrical capacity [MWel] Heat capacity [MWth] Fuel type Electrical efficiency [%] Total efficiency [%] Fixed operation & maintenance cost [V/kWel*a] Variable operation & maintenance cost [V/MWh*a] Specific CO2 emissions [t CO2/MWhel] Investment cost [V/kWel] Depreciation period [a] Capacity utilization, Mean power generation [%] Standard deviation Distribution Capacity utilization, Mean heat generation [%] Standard deviation Distribution

Coal-fired CHP

CCGT-CHP

Engine CHP

Micro-turbine CHP

800 400 Coal 0.42 0.65 50 2 0.750 1400 40 68 8.4 Normal 45 5.7 Normal

400 400 Natural gas 0.50 0.80 36 1 0.360 1200 30 68 12.8 Normal 45 5.7 Normal

2 2.5 Natural gas 0.36 0.90 40 0.7 0.495 1300 20 45 5.7 Normal 45 5.7 Normal

0.05 0.1 Natural gas 0.30 0.85 40 0.5 0.660 1500 20 45 8.5 Normal 45 5.7 Normal

Source: Operational data derived from existing E.ON plants. Investment costs derived from the latest finalized newly built projects of its kind in Germany (e.g. E.ON hard coal plant Datteln, E.ON CCGT plant Irsching).

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4.2. Results of the economic evaluation

Table 5 Commodity price assumptions for the NPV calculation. Price variable

Mean

Powera Hard coalb Natural gasc CO2 allowancesd Heate

50.24 9.22 18.61 17.61 20.68

Standard deviation V/MWhel V/MWhth V/MWhth V/t CO2 V/MWhth

19.43 2.66 6.54 5.28 7.27

V/MWhel V/MWhth V/MWhth V/t CO2 V/MWhth

Price correlation coefficient

Powera

Hard coalb

Natural gasc

CO2 allowancesd

Powera Hard coalb Natural gasc CO2 allowancesd

1.000 0.567 0.692 0.556

0.567 1.000 0.766 0.820

0.692 0.766 1.000 0.727

0.556 0.820 0.727 1.000

a b c d e

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EEX Price Index: Phelix Day Base. API#2 Index: ARA Quarterly Futures Price. EEX Gas Spot Market EGT. CO2 Allowance Price Phase 2. Calculated on the basis of a gas-fired boiler with an efficiency of 90%.

same technology. The reason for this lies in the fact that only a part of the fuel energy is utilized for power generation, and the rest is utilized for heat production. The total efficiency of CHP installations depend significantly on the heat utilization at the respective site and cannot be defined generally. The values for the total efficiency in Table 4 are average values that are reached in existing E.ON plants of the respective technology. Technical parameters, such as the installed capacity, are determined by the plant design and cannot be changed easily, while operational parameters, such as utilization or operation and maintenance costs, can be influenced through plant operation. 4.1.2. Commodity price assumptions The commodity prices used in our model are taken from the German commodity markets. As input parameters we take the historical commodity prices and correlations, reported in Table 5. Means and standard deviations of the price distributions are derived from daily market prices in the time period from October 1, 2007 to September 30, 2010. All commodity prices are characterized by a log-normal distribution. As transparent market data of heat prices in Germany are unavailable, we derive the heat price based on alternative costs for heat generation. This is a common approach, but in principle there are several options to determine the price for heat produced in CHP installations (Phylipsen et al., 1998; Rosen, 2008). In our model, we assume a heat production according to the reference efficiency of stand-alone steam production. As reference efficiency we take 90%, which corresponds to the reference values as mentioned in the European Commission’s Decision 2007/74/EC (CEC, 2006). In our model, we further assume a linear increase in commodity prices with a constant rate of 2% per year.

Our economic evaluation is based on a discounted cash-flow model that generates net present value (NPV) distributions of various CHP technologies feeding into DH systems. The discounted cash-flow method values an asset by estimating future cash flows and discounting them back to the present value. This is a common approach, which has already been used for several applications in the context of power generation facilities (Roques, 2008). Our model considers all costs and revenues of the CHP plant and generates an annual cash flow that is subsequently discounted with a discount rate of 8%. For large-scale plants that are subject to the EU ETS, we consider two different allocation mechanisms for CO2 allowances. First, we calculate the NPV distribution based on the double benchmark principle, as currently implemented in the German National Allocation Plan. In a second step, we assume an allocation according to the principles stipulated in EU Directive 2009/29/EC. Both options differ concerning the treatment of the CO2 allowances costs ðCCO2 Þ that are determined according to the following formula:

CCO2 ¼ ðE  AX Þ$pCO2 ;

(3)

where E stands for the total emissions of the CHP plant and AX represents the total free allocation of emission allowances in the considered period, as defined previously in (1) and (2) for various allocation mechanism. pCO2 is the stochastic price of the allowances characterized by mean and volatility, as reported in Table 5. For the previously defined CHP technologies, we compute the probability distributions of the NPVs by running a Monte Carlo simulation with 50,000 runs. Hereby, electricity, fuel, and CO2 prices are modeled by random variables on the basis of historical volatilities (see Table 5). The capacity utilization is also considered as a random variable (see Table 4). The NPV is very sensitive to commodity prices, plant utilization, investment costs, and discount rate. The results of the model are shown in Table 6. We find that the economic attractiveness of large-scale CHP installations for district heating is dramatically reduced through the modified allocation principles, as established in EU Directive 2009/29/EC. The internal rate of return (IRR) of new coal-fired CHP steam plants is reduced from 9.0% to 57.6% and the IRR of gas-fired CCGT-CHP plants from 9.8% to 38.0% (under the assumption that commodity price levels remain at the same level). Thus, the intended allocation principles for the third ETS period render investments in large-scale CHP installations for DH largely uneconomical. In contrast, NPVs and IRRs of small-scale CHP units for local heat supply are not affected by the modification of the allocation principles, as their thermal capacity is far below 20 MWth, which implies that these small-scale systems are not covered by the EU ETS. Fig. 4 illustrates the effect of an allocation according to EU Directive 2009/29/EC on the NPV distributions of large-scale CHP

Table 6 Results from the NPV calculation for the different technologies. Allocation mechanism

Coal-fired CHP Gas-fired CCGT-CHP Engine CHP Micro-turbine CHP

Double benchmark According to EU Directive 2009/29/EC Double benchmark According to EU Directive 2009/29/EC n.a. n.a.

Expected mean of NPV

Standard deviation of NPV

Internal rate of return (IRR)

[V/kW]

[V/kW]

[%]

126 806 117 456 46 23

1205 1895 1401 1571 598 413

9.0 57.6 9.8 38.0 5.8 1.5

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Fig. 4. Effect of the modified allocation rules on the NPV distribution of large-scale CHP plants for DH applications (discount rate 8%).

plants for DH applications. For both technologies investigated, the expected mean of the NPV decreases from 126 V/kW (117 V/kW) to 806 V/kW (456 V/kW) for coal-fired CHP plants (gas-fired CCGT-CHP plants) if the modified allocation rules are applied. For coal technology, the standard deviation of the NPV increases from 1205 V/kW to 1895 V/kW. Due to the reduction of freely allocated certificates, plant operators need to purchase significantly more emission allowances at the CO2 market. This increases the exposure to volatile EUA prices and, subsequently, the standard deviation of the NPV. For the CCGT-CHP technology, this effect is less significant, as the carbon intensity of natural gas is lower. Consequently, CCGT technology receives relatively to the output more free allowances and thus less EUAs need to be purchased at volatile market prices. During the third phase of the EU ETS the decreasing amount of free allocation could have a substantial

impact on commodity price levels (Fuss et al., 2009). To illustrate the effects of changed commodity prices on the NPVs we conducted a sensitivity analysis. The results of the analysis (with a variation of 10%) are shown in Fig. 5. For all technologies investigated except micro-turbine CHP the electricity price level has the highest impact on the NPV. The dependence of the NPV on the market price for European emission allowances is significantly increased after the introduction of the new ETS Directive. This effect is illustrated in Fig. 6. Under the assumption that free allowances are allocated according to the double benchmark principle, the NPV at an EUA price level of 30 V/t CO2 is for both technologies in the range of 0 V/kW. If the modified allocation rules according to EU Directive 2009/29/EC are introduced, the NPV is highly negative at an EUA price level of 30 V/ t CO2, an effect that is more significant for coal-fired CHP (1437 V/ kW) than for CCGT-CHP (1120 V/kW). 5. Impact of modified allocation rules on investments in new CHP plants for DH In this section, we investigate the impact of the modified EU ETS framework on investment decisions in new CHP plants for DH applications. The investigation is based on a real options (RO) model that uses specific spreads to quantify the uncertainty of investment decisions. In contrast to the static “now or never” proposition of the NPV analysis, the RO approach includes the possibility of delaying an investment under uncertainty and considers the value of waiting as part of the decision-making problem. 5.1. Real options approach

Fig. 5. Sensitivity analysis: Impact of a 10% change of the respective variable on the NPV of CHP technologies in the third ETS period.

The RO approach, as introduced by McDonald and Siegel (1986), Pindyck (1998, 1991, 1993), and Dixit and Pindyck (1994), is an often applied method to evaluate investment decisions under uncertainty. During the last few years, in a number of studies, RO theory has been applied to decision-making problems in the energy sector and here especially on investment decisions in new power generation assets. It would be far beyond the scope of this article to provide a complete overview of the available approaches proposed in the relevant literature that differ in model design,

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55

Fig. 6. NPV of large-scale CHP plants for DH as a function of the price for EU emission allowances.

modeling of stochastic variables, and choice and definition of input parameters (for a more detailed overview, see Westner and Madlener, 2011b). We just want to briefly review some recently published research results that apply real options on CHP generation, consider the impact of emission trading systems on investment decisions, or use the spread as input parameter for the RO evaluation. Deng et al. (1999) developed for the first time a spread-based RO model to evaluate electricity derivatives. In their approach, which is based on future contracts for electricity and fuels, they apply geometric Brownian motion as well as mean reverting price processes to characterize spark spread options. The investigation of Deng et al. was done before the introduction of emissions trading systems and, therefore, does not consider the costs of carbon allowances. Laurikka and Koljonen (2006) investigated the impact of the EU ETS on investments in power plants. Their research takes specific spreads between commodity prices as input parameter and focuses on the situation during the first trading period in Finland. Under consideration of the EU ETS, they investigate two options: the option to postpone the investment and the option to alter plant operation. They consider a hypothetical condensing power plant with an electrical capacity of 250 MWel and conclude that the impact of emissions trading depends not only on the expected level of prices for carbon allowances, but also on their volatility and correlation with electricity and fuel prices. Wickart and Madlener (2007) applied RO theory on CHP generation and investigate the decision-making problem of an industrial firm, considering whether to invest either in CHP generation or in heat-only production. According to their findings, simplistic NPV calculation can be misleading when lu et al. (2008) estimating economic CHP potentials. Kumbarog analyzed diffusion prospects of new renewable power generation technologies by applying RO theory. They include learning curve information in their model and consider price uncertainties concerning the wholesale electricity price and the input fuel prices through stochastic processes. The results of their research show that the flexibility to delay irreversible investments can profoundly affect the diffusion prospects of renewable power generation technologies. Siddiqui and Maribu (2009) developed

and applied an RO model for microgrids that consist of small-scale CHP applications. In order to reduce the risk exposure, they investigate various investment strategies. They come to the conclusion that a direct investment strategy in microgrids is more beneficial for low levels of gas price volatility, whereas a sequential strategy is preferable in the case of high price volatility. Fleten and Näsäkkälä (2010) and Näsäkkälä and Fleten (2005) applied RO theory to new gas-fired power plants in liberalized energy markets with volatile electricity and natural gas prices. Their model is based on the spark spread, defined as the difference between the price of electricity and the cost of gas used for power generation. They derive the value of operating flexibility, and find thresholds for energy prices that are optimal for entering into investments. Westner and Madlener (2011b) applied a spreadbased RO approach to analyze the decision-making problem of an investor who has the choice between an irreversible investment in a condensing power plant without heat utilization and a plant with CHP generation. They find that the specific characteristics of CHP plants have a significant impact on the option value and, therefore, on the optimal timing to invest. In this paper, we apply RO theory on investments in fossil-fueled CHP plants that feed into DH systems. The special focus hereby lies on the allocation mechanism for CO2 emissions allowances. The topic investigated seems to be very specific, but the applied evaluation model that is based on clean spreads has a wider relevance and can be transferred to further kinds of generation technologies or other applications that are subject to the EU ETS. 5.2. Model description The RO model that we apply is based on the research of Dixit and Pindyck and uses the specific spread as input parameter. The specific spread per MWh is the difference between the prices of the produced outputs (power and heat) and the costs of the input factors (e.g. fuel and emission allowances) and represents the contribution margin that a plant operator earns for converting fuels into electricity. Specific spread, plant utilization, and fixed costs define the profit of a CHP plant. In our simplified model, we consider plant utilization and fixed costs as constant parameters

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Table 7 Characteristics of the specific spread for various generation technologies. Expected “clean” spread

Allocation mechanism

Coal-fired CHP

Double benchmark According to EU Directive 2009/29/EC Double benchmark According to EU Directive 2009/29/EC n.a. n.a.

Gas-fired CCGT-CHP Engine CHP Micro-turbine CHP

Standard deviation of the “clean” spread

[V/MWh]

[V/MWh]

15.27 7.94 9.87 5.12 8.88 3.71

12.75 17.67 16.06 18.21 14.07 14.31

Source: Own calculation, based on Eq. (4), with input parameters reported in Table 5.

without volatile deviations to make the effect of changes in the allocation principles more transparent. With this approach we assume that there are no impacts of the adjusted ETS rules on the operational characteristics and that the average utilization is constant over the lifetime of the plant. Our model considers the costs of CO2 emissions, as we take the so-called clean spreads for our investigation. The specific spread (S) of a CHP plant in V per MWh is defined as

S ¼ PE þ RH þ PCHP 

CF

hel

 CCO2 ;

(4)

where PE is the market price of electricity in V/MWh, RH represents the additional revenues through heat sales and PCHP represents the governmental promotion for CHP generation (both in V/MWh), CF denotes the fuel costs in V/MWhth and hel the electrical efficiency of the CHP plant. CCO2 denotes the cost of CO2 emissions as defined in (3). In our approach, the specific spread contains governmental subsidies that are paid for highly efficient CHP plants in several member states of the EU (Westner and Madlener, 2010). Note that the specific spread S of a generation technology is affected by the development of prices for electricity, fuel, and CO2 allowances in competitive commodity markets, and can take positive and negative values. Based on historical commodity prices, as described in Table 7, we derive the characteristic parameters of the specific spread by technology in Germany during the time period October 1, 2007 until September 30, 2010. The historical development of the specific spread is best described by a normal distribution. In our investigation, we assume that the specific spread of technology i evolves according to the geometric Brownian motion

dSi ¼ aSi dt þ sSi dz; Si

(5)

where aSi and sSi are constants that describe the drift and volatility of the specific spread of generation technology i, dt is an infinitesimal time increment, and dz is the increment of a Wiener process. The decisive parameter in our RO model is the volatility of the aggregated annual spread. Therefore, in the following, we ignore the drift and assume aSi ¼ 0. The decision to invest in a power plant can be interpreted as an optimal stopping problem that can be solved by using a dynamic programming approach. The value of the option to invest F(V) in a power plant is given by the Bellman equation

rFðVÞdt ¼ E½dFðVÞ:

(6)

This equation implies that holding an option with the value F(V) over the period dt yields an expected gain of E[dF(V)]. The expected gain needs to be equal to the return rF(V)dt, where r represents the discount rate of the investor. By applying Itô’s Lemma, we derive the partial differential equation

dFðVÞ ¼

1 00 F ðVÞðdVÞ2 þF 0 ðVÞðdVÞ: 2

(7)

Itô’s Lemma is an indispensible tool for working with continuous time random processes. It is applied to find the differential of a function of a stochastic process (Dixit and Pindyck, 1994). Substituting (5) into (7) and given that E(dz) ¼ 0, we obtain

E½dFðVÞ ¼

1 2 2 00 s V F ðVÞdt þ aSi VF 0 ðVÞdt: 2 Si

By substituting (8) into (6), we derive

Fig. 7. Option values of large-scale CHP plants for different CO2 allocation mechanisms.

(8)

G. Westner, R. Madlener / Journal of Cleaner Production 20 (2012) 47e60

57

Fig. 8. Option value of large-scale units for CHP-based DH in comparison to small-scale installations.

1 2 2 00 s V F ðVÞ þ aSi VF 0 ðVÞ  rFðVÞ ¼ 0: 2 Si

(9)

In addition, Vi must satisfy the following boundary conditions:

Fð0Þ ¼ 0

(10)

FðV*Þ ¼ V*  I

(11)

0

F ðV*Þ ¼ 1:

(12)

Condition (10) arises from the observation that if the value goes to zero, it will remain zero (this is an implication of the stochastic process described in (5)). V* represents the critical plant value at which it is optimal to invest and (11) is the value-matching condition that defines the net payoff (V*I) of the investor. Equation (12) is the so-called smooth-pasting condition that guarantees that the gradient of the first deviation is equal at the exertion point. To get the value F(V) of the investment option, we need to solve (9) subject to the boundary conditions (10)e(12). Equation (9) represents a second-order homogeneous differential equation that is linear in the dependent variable F and its derivatives. The general solution can be expressed as a linear combination of two independent solutions and written as

FðVÞ ¼ A1 V b1 þ A2 V b2 ;

(13)

where A1 and A2 are constants and b1 and b2 are the roots of the quadratic function

1 2 s bðb  1Þ þ ðr  asi Þb  r ¼ 0: 2 Si

(14)

Note that b1 and b2 depend on the parameters aSi and sSi of the differential equation and on the discount rate of the investor r in the following way:

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi #2 u" a 1 aSi u 1 2r bi ¼  2  t 2Si  þ 2 >1; 2 sS sS 2 sS i

i

i ¼ f1; 2g:

(15)

i

In our case, boundary condition (10) implies that A2 ¼ 0, so that the solution takes the form

FðVÞ ¼ AV b1 :

(16)

The remaining boundary conditions can be used to define the two remaining unknowns: the critical value V* at which it is optimal to invest, and the constant A:

V* ¼

b1 I; b1  1

(17)

A ¼

V*  I ðV*Þ

b1

¼

b1 1

ðb1  1Þ b1

ðb1 Þ $I b1 1

:

(18)

Based on the given equations, it is possible to analytically determine option values of the above-described CHP technologies under various assumptions concerning the EU ETS allocation mechanism. 5.3. Discussion and interpretation of the results In this section, we discuss the results gained with the described spread-based RO model and derive consequences for utilities that intend to invest in new CHP plants for DH applications. We further compare large-scale installations with small-scale decentralized CHP units that are not subject to the EU ETS and then draw some conclusions on how the diffusion of these technologies may be influenced by the adjustments of the emissions trading system. 5.3.1. Impact of the EU ETS on investments in large-scale CHP applications for DH The option value of irreversible investments in CHP generation technologies gives an indication of the uncertainties and risks that need to be covered by an investor. In principle, investments with high option values are more likely to be postponed or even canceled in comparison to investments with low option values. The results of our model show that option values of large-scale CHP applications for DH, irrespective of whether they are coal-fired or gas-fired, increase through the intended modifications of the allocation principles, as described in EU Directive 2009/29/EC. In other words, the implementation of the modified allocation rules for CO2 emission allowances generally increases uncertainties and risks for utilities that intend to invest in the CHP units for DH applications considered. This effect can be explained with the lower free allocation and the subsequent need to auction or buy more allowances on the commodity market. Therefore, the exposure to volatile commodity prices for CO2 allowances is increased and consequently also the option value of the investment project. For coal-fired CHP units, as defined in Table 4, the difference in the option value between an allocation according to double benchmark and an allocation according to EU Directive 2009/29/EC amounts to 6.56 V/MWh at the strike price of 18 V/MWh. For gas-fired CCGT-CHP plants, the difference in the option value amounts to 2.96 V/MWh at a strike price of 16 V/MWh. The strike price corresponds in this context with the average specific spread that needs to be achieved during the plant lifetime to cover the investment costs. This result, as illustrated in Fig. 7, clearly shows that the economics of carbon-intensive technologies, such as hard coal-fired CHP plants, are more negatively affected by the intended adjustments of the EU ETS rules than gasfired installations.

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5.3.2. Impact of modified EU ETS principles on the diffusion of small-scale CHP units In a next step, we compare the option values of large-scale CHP plants for DH application with small-scale units (engine CHP and micro-turbine CHP). These distributed small-scale CHP units with a thermal capacity below 20 MWth are not subject to the EU ETS and, therefore, not affected by the modified allocation mechanism introduced post-2013. While the option value of investments in large-scale CHP units increases through the modified allocation rules for the third ETS period, the option values of the small-scale applications are not affected. Fig. 8 illustrates the difference in the option values of CHP installation for DH of different size. Independently of the specific spread, the option value of small-scale CHP units is constantly below the option value of the large-scale plants considered. Note that this effect is independent of the fuel type of the large-scale plant. Consequently, investments in small-scale CHP units imply lower risks and uncertainties in comparison to largescale plants. The preference of investors for small-scale distributed heat production might therefore increase. 6. Conclusion In this paper, we have investigated the impact of modified EU ETS principles for the third emission trading period on the economics of CHP plants that feed into DH systems. We apply a discounted cashflow model to evaluate the economic effects of the EU ETS principles as well as a spread-based real options model to analyze their impact on investment decisions in new CHP plants for DH applications. The results of the discounted cash-flow model show clearly that the NPVs of large-scale CHP plants are considerably reduced compared to the situation with the current allocation mechanism for the second ETS period. The RO analysis leads to the conclusion that the modification of EU ETS principles increases the option values of large-scale CHP plants compared to the current situation. This is equivalent to an increase in risks and uncertainties for potential investors. Consequently, the economic attractiveness of large-scale CHP generation that feeds into DH systems is negatively affected by the introduction of the modified allocation principles stipulated in EU Directive 2009/29/EC. This effect is more significant for coalfired plants than for gas-fired CCGT units, as the new allocation rules define a uniform benchmark for heat generation that is based on the natural gas benchmark and does no longer differentiate between the carbon intensity of the applied fuel. The topic investigated in this paper seems to be very specific, but the applied evaluation model can be transferred to other kinds of power generation and other industries that are subject to the EU ETS. Our model simplifies the complex economic evaluation of CHP

generation in some points. As input parameter for the Monte Carlo simulation we take e.g. annual averages of the commodity prices instead of hourly parameter values. We are also aware that the applied real options model, which is based on historic market data, does not consider future developments and dependencies of commodity prices. But the gained results provide at least an indication of how the economics of different CHP applications for DH are affected by the new allocation rules. In principle both kinds of CHP installations (large-scale and small-scale units) contribute with their specific characteristics to increase energy efficiency and reduce total CO2 emissions. Decentralized small-scale CHP minimize distribution losses for power and heat (Tsikalakis and Hatziargyriou, 2007). In the practical application they are still at an early stage, but their acceptance will likely increase further and economics of scale will also increase their economic attractiveness (Lovins et al., 2002). A coordinated control of small-scale CHP units, e.g. according to the commodity price levels, requires much effort and is complex due to the high degree of market fragmentation. Therefore, also large-scale CHP plants with high efficiency rates and better operational flexibility are important. These plants can be controlled more easily, so that it is possible to optimize their operation according to the current prices at the commodity markets. In particular, the shift from backpressure to condensing mode in times with high power prices or the usage of cheap heat storages anywhere in the urban DH network provide a huge potential for optimization. The design of the allocation principles for the third ETS period should account for these benefits. Otherwise, the implementation of the new allocation rules could lead to a situation where highly efficient large-scale CHP plants are gradually replaced by less efficient separate heat and power generation in boilers respectively fossil-fueled condensing plants. These technologies are the most likely substitutes for large-scale CHP installations and have for many applications lower specific investment costs. A possible compensation for large-scale highly efficient CHP installations could be conceded by a more beneficial allocation of allowances, comparable to the commonly accepted mechanism for industries that are exposed to carbon leakage. Another possibility would be an EU-wide harmonization and improvement of promotion schemes for highly efficient CHP installations. Such instruments are already applied in many EU member states but in their current design they are not able to compensate for the adverse impact that CHP installations face through the modified EU ETS principles.

Appendix A1. Results financial model

Table A1 Descriptive statistics of the NPV for different CHP technologies (in V/kWel). Coal-fired CHP Allocation mechanism Runs Mean Mode Standard deviation Variance Skewness Kurtosis Coeff. of variability Minimum Maximum Range width

Double benchmark

Coal-fired CHP According to EU Directive 2009/29/EC

50,000 126.3

Double benchmark

50,000 806.2

According to EU Directive 2009/29/EC

50,000 117.1

50,000 456.0

e

e

e

e

1204.7 1,451,213.7 1.2 5.5 9.5 3350.6 9600.1 12,950.7

1895.1 3,591,574.6 1.1 5.9 2.3 4252.9 9871.7 14,124.6

1401.4 1,963,975.6 0.7 5.2 12.0 6708.2 11,235.5 17,943.6

1571.2 2,468,135.3 0.5 4.9 3.4 8611.6 10,920.7 19,532.2

Engine CHP

Micro-turbine CHP

n.a.

n.a.

50,000 45.7 e 598.2 357,801.9 0.6 5.1 13.1 3199.1 5280.6 8479.8

50,000 23.4 e 413.0 170,569.0 0.0 4.6 17.6 1320.1 1470.9 2791.0

G. Westner, R. Madlener / Journal of Cleaner Production 20 (2012) 47e60

59

A2. Results real options model Table A2 Option values in dependence of the specific spread (in V/MW). Specific spread

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

0.0 0.0

2.3 4.7

5.3 9.5

8.5 14.3

12.0 19.1

15.7 23.9

19.5 28.8

23.5 33.6

27.5 38.4

0.0 0.0

3.7 4.9

7.8 9.8

11.9 14.8

16.2 19.7

20.5 24.6

24.9 29.6

29.3 34.5

33.7 39.5

0.0 0.0

2.7 2.5

6.0 5.5

9.6 8.7

13.3 12.1

17.1 15.6

21.1 19.3

25.1 23.0

29.2 26.7

Allocation mechanism Coal-fired CHP plant

Gas-fired CCGT-CHP plant Engine CHP Micro-turbine CHP

Double benchmark According to EU Directive 2009/29/EC Double benchmark According to EU Directive 2009/29/EC n.a. n.a.

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Günther Westner was born in Germany in 1976 and received his degree in electrical engineering from the Technical University of Munich in 2005 and his degree in business economics from the University of Hagen in 2007. Currently he works for E.ON, one of the leading European utility companies, and is project manager for combined heat and power application in the industrial sector. His research interests include energy economic questions related to CHP generation, investment decisions and portfolio optimization.

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Reinhard Madlener was born in Austria in 1964. He graduated in economics and business administration at the Vienna University of Business and Economics (WU Wien) and received his PhD in economics and the social sciences at the WU Wien in 1996. He specialized soon in energy and environmental economics. He was assistant professor at the Centre for Energy Policy and Economics (CEPE) at the ETH Zürich

(2001e2007), and lecturer at the University of Zürich (since 2003). In 2007 he was assigned as a full professor in energy economics and management and first director of the Institute for Future Energy Consumer Needs and Behavior (FCN) at the RWTH Aachen University. One of his special research interests is in the economics of technological innovation and diffusion for sustainable development.