Turkey Green Growth Policy Paper: Towards a Greener Economy

6.3 Environmental Pollution and Instruments of Abatement

Two types of environmental pollutants are explicitly considered in the model:

• 
  Air pollution, in the form of CO2 and PM10, from three main sources: (a) industrial processes; (b) (primary and secondary) energy usage; and (c) household energy use. These can be reduced in a variety of ways (fuel substitution, improved energy efficiency).
  
• 
  
  Table 6.1 Environnemental Tax Instruments Modelled
  
  
  Waste discharges (both solid and liquid), also from three main sources: (a) urban waste (formulated as a ratio of urban consumption); (b) waste from industrial processes; and waste from water use in agricultural production. The model assumes a fixed quantity of waste generation per unit of output, so as with agriculture, reductions in these waste streams through policy will have proportionate negative effects on output.
  

The main greening instrument used, a pollutant tax/fee, is applied on a per-unit basis (to CO2 emissions, production, intermediate input usage, and consumption, respectively) and to PM10 and waste generated. Table 6.1 lists all the tax instruments used in the model. The revenues generated are either directly added to the revenue pool of the government budget, or directed towards a particular set of green job creation or innovation activities. The set of environmental tax/fee instruments are tabulated in Table 6.1. This set-up is stylized, and results should be interpreted with care. Model limitations and possible extensions are discussed below.

6.4 Data

The model is built around a multi-sectoral social accounting matrix (SAM) of the Turkish economy based on TURKSTAT 2002 Input Output Data. The I-O data is re-arranged accordingly to give a structural portrayal of intermediate flows at the intersection of the commodities row and activities column in the 12-sector 2010 macro-SAM. More details of the sectoral input-output flows of the macro SAM in correspondence with the TURKSTAT I-O data are given in Annex 2).

6.5 Policy Scenarios and Analysis

The policy analysis for investigating the macroeconomic impacts of alternative “greening measures” or “green policy instruments” is divided in two parts: greening the urban economy, and greening the rural economy.

This is done through two policy packages:

In terms of solid waste, of the total amount generated by industry annually (estimated at 12.5 million tons) about 40% of it is known to be recovered and/or reused (and thus “greened”). The remaining 7.2 million tons would require further treatment. For the household sector, 26.1 million tons is the total amount of solid waste generated annually. Particular matter intensities (PM10) are reflected in concentrations exceeding both the Turkish standard of 60μg/m³ (an all other more stringent international standards).

In the first set of policy analysis, the policy targets reflect EU directives on waste management; wastewater and air pollution that are the main vehicles for greening solid and industrial waste, and PM10 concentrations are over the base-path. The analysis also includes targets provided in Turkey’s National Climate Action Plan (NCAP) which calls for a 25% reduction in the quantity of landfill-biodegradable waste by 2015, 50% by 2018, and 65% by 2025. The NCAP further calls for reaching 100% of target for the disposal of municipal waste in integrated SW disposal facilities, complemented by waste recycling programs consistent with the EU Integrated Waste Management directives.

To summarize, with reference to Table 6.1, the policy package in the first scenario is made up of a total seven new greening measures: taxes on PM10 emissions applied to industrial processes, industrial energy combustion, and private household energy consumption; and urban solid waste and waste water fees applied to industrial sectors and households.

• 
  Confronted with environmental taxes that alter their own efficient production decisions and input mix, the private sector would initiate a host of adjustments, including the adoption of technologies that help reduce pollution intensities per unit of output produced, as well as using inputs more efficiently (e.g., energy input, water). Given the new costs imposed on production by environmental taxes, in order to remain competitive the private sector will look for adopting less polluting and more input-efficient technologies (e.g., the case of the iron and steel industry), as well as target gains in productivity through innovation (e.g., the case of the automotive industry).
  

• 
  From the public sector side, in addition to needing to ensure that the enabling environment for private sector investment (e.g., labor market, finance, innovation policy) is conducive to the acquisition/development of green technologies and innovation, there is also the need to consider mechanisms for allocating the green tax revenues in such as way as to limit their economic burdens. The modeling analysis also takes account the productivity improvements expected to result from of the overall population’s lower exposures to different pollutants (not just specific workers).
  

Policy scenario 2 also incorporates the impacts of reduced pollution intensity sector productivity based on improved health from mitigating air pollution to achieve EU air quality standards, as this is one of the driving factors of a greener economy (Box 6.2). A detailed environmental health valuation based on air pollution levels and the historical growth and population trajectories suggests that without any intervention, the growth in PM10 in Turkey will cost between 1% - 4.5% of GDP from 2010-2030 in the absence of control measures (Table 6.2). Both the health impact and productivity gains should be considered as lower bounds since other environmental health issues (e.g., from water-borne diseases and the fact that the number one water issue in Turkey is related to low level of wastewater treatment) have not been accounted for.

Box 6.2 Generating green jobs and productivity enhancements through pollution abatement activities

A three-step approach is used as part of the new environmental component of the CGE model.

Step 1: Since the urban wage rate is given in real terms (at W*), added employment for wastewater and solid waste mitigation can be written as:

W*L_G,J = _J (taxrev_J) (1)

In (1) above, L_G,J stands for new employment at the j-th category of environmental abatement activities (urban solid waste treatment across industry and households, and urban water treatment across industry and households), and taxrev_J refers to the corresponding tax revenues collected from the respective abatement sector J. Realistically, since not all tax revenues are likely to be channeled for the new employment wage fund, through the parameter _{J ,} a portion of the aggregate tax revenues are used for sustaining the wage fund, and the rest accrues to the public revenues as residual. Wage income from this added green employment accrues to the private disposable income.

Step 2: Reducing pollution intensities through the activities of the added green workers. The new employment generated (L_G,J) is used for pollution abatement activities at the respective industry to reduce the PM10 and waste intensities, _J through the exponential form (2) below, where in the numerical implementation of the model scenarios, the structural parameter α_J is arbitrarily taken as 1,000:

(2)

Step 3: Enhancing productivity is further modeled as gains due to improved health from mitigating PM10 pollution, through the following production shift:

(3)

Where the Hicks-Neutral productivity coefficients are adjusted upwards given the rate of abatement gains, , and the parameter _J controls the structural effectiveness of this relation.

This functional form is calibrated to a standard that could meet the 40µg/m³ EU standard, implying an expected gain of some 2.0% of GDP from PM10 mitigation, which is achieved by setting the structural parameter _J at 0.002, implying a modest productivity gain is a modest 0.01% per annum.

Source: CGE model and analysis (Annex2)


Generation of productivity enhancements through earmarking carbon tax revenue for R&D and innovation. In addition to the experiments of the first scenario, which were focused on internalizing externalities through taxes/fees to mitigate air, water, and solid waste pollution, and earmarking funds for green jobs, the second scenario introduces a policy of mitigating CO2 emissions through a tax instrument and earmarking funds for innovation. The base path trajectory reveals that aggregate CO2 emissions (currently 369 million tons) will reach a total of 983.7 million tons by 2030. Given the projected GDP, by 2030 CO2 intensity-(emissions per dollar GDP) is estimate to reach 0.59 kg/$ (in fixed 2010 prices) (currently 0.72 kg/$). To effectively reduce the CO2 emissions, a carbon tax is imposed on polluters, as was done for PM10 above. The distinguishing characteristic of the policy intervention is the use of
Box 6.3 Environmental Regulation and Innovation: The Porter Hypothesis

While it is generally understood that tighter environmental standards will be costly, at least in the short to medium term, the Porter Hypothesis (Porter and van der Linde 1995) holds that properly designed environmental regulation—in particular market-based instruments such as taxes or cap-and-trade emissions allowances—can trigger innovation. Recent research is providing insight into the relevance of the PH. While on the theoretical side, more arguments are emerging that try to justify the hypothesis, empirically, the evidence only supports the “weak” version (i.e., stricter regulation leads to more innovation). So far at least, there is no significant empirical evidence of the “strong” version of the hypothesis (i.e., stricter regulation enhances business performance—or win-win).

Source: Ambec et al. (2001)

Source: CGE model and analysis (Annex2)

The following are the main findings from the analysis.⁴⁵

• 
  A significant reduction in the level of pollution intensities, consistent with the standards set forth in the relevant EU Directives.
  

• 
  But this is also accompanied by a relatively significant reduction in the growth potential of GDP (10-14% over its 2030 real value). This is indicative of the trade-offs involved, as pollution abatement costs increase the price of doing business in the absence of any adjustments in abatement technology, in the presence of the given historical rigidities (especially in labor markets). It cannot be over-emphasized that the figures show a relative decline in GDP relative to what would be achieved by 2030 without the greening measures. However, the results do NOT imply an absolute contraction of GDP due to environmental policy. Indeed, GDP in 2030 is 2.4 times its 2010 base value with the greening measures (not including absent green jobs and innovation-induced TFP gains), versus 1.27 times without any green measures (base path business as usual); in terms of average annual growth rates, the difference is 4.4% versus 5.0%.
  

• 
  Note also that these GDP figures do not include productivity gains or any other health benefits from pollution, or other green benefits. Nevertheless, the combined impact of the wastewater, solid waste, and air pollution taxes may raise concerns for fiscally-concerned decision makers.
  

• 
  A disaggregated application of individual pollution taxes allows a differentiation of their impacts and reveals that: (a) The tax on PM10 alone has a relatively small negative impact on GDP (less than 0.5%) and 3.8% pollution reduction; and when health-related pollution abatement productivity effects are accounted for, it’s overall impact is positive (+4.6% GDP and 21% pollution reduction). (b) Taxing solid waste has the highest negative impact on GDP. This is due to the combined effects of two factors: current solid waste disposal levels are very low (thus the size of the intervention to meet the set target is very large, leading to a 44% reduction in pollution); and since these waste flows are modeled as ratios of household consumption expenditures, the waste tax thus has a direct negative effects on consumption demand. The costs would be lower with a more realistic, flexible relationship between consumption and the generation of solid waste. In comparison, the economic impacts of the wastewater tax are much lower because both the target coverage and the tax rate are lower, leading to a 19% reduction in pollution. (iii) The CO2tax leads to a 9% abatement and a 7.4% reduction of GDP. These disaggregated results indicate that air pollution and wastewater could be prioritized within the green urban scenario because of their positive health and productivity effects and their low economic impact.
  

Box 6.4 Generation productivity enhancements through earmarking carbon tax revenue for R&D/innovation.

As part of the new environmental component of the CGE model, a two-step approach is used to model the productivity gain from innovation stemming from earmarking CO2 emission tax revenues.

Step 1: innovation-driven productivity gains are modeled as:

₍₄₎

These gains in the productivity parameter AX_SS pertain only to the set of strategic sectors (SS). In addition, innovation activities are assumed to use an abatement technology that saves on the use of energy inputs, thereby lowering the CO2 intensities arising from energy combustion.

Step 2: Similar to the specification in (2) above, the CO2 emission intensities in energy use (within the SS-sectors) are reduced through innovation funded by the carbon tax revenues:

₍₅₎

The ratio of aggregate R&D expenditures to the GDP currently stands at 0.7%. The Strategy Document calls for an increase of this ratio to 3% of the GDP by 2023.

The two key “enhancement parameters,” φ and ϕ, are set here to calibrate the TFP gains to generate an additional gain of 0.6% over the historically observed path. Moreover, this set-up is very optimistic in that it incorporates both general productivity increases and implicit increases in the specific productivity of low-carbon energy sources. Further work on a more realistic formulation of innovation policy in a CGE analysis for Turkey would be highly desirable.

Source: CGE model and analysis (Annex2)

Figure 6.2 Summary of policy scenarios for greening the urban economy

• 
  Total employment would increase so does the wage income to the private sector. With about 600,000 new jobs “created” in the green activities in the Turkish economy as a whole⁴⁸ .Green wages reach almost 1.5% of aggregate private disposable income. An example where the potential for green jobs has been identified through the study is energy efficiency in buildings, where specific incentive schemes which could be financed from green taxes, could result in some 110,000 jobs by 2023 over the base case where no incentives are provided (Table 6.3). In addition, if one accounts for health-related productivity gains from PM10 abatement, GDP from all three urban greening taxes leads to GDP only 1.3% by 2030 below its baseline growth path (again, with NO reduction in GDP – simply a lower rate of growth).
  

• 
  Pollution intensities are significantly reduced to the levels consistent with the standards set forth in the relevant EU Directives.
  

• 
  When tax policy on pollution is further complemented by adding a carbon tax to control CO2 emissions, but these tax revenues are used for R&D funding and innovation solely in the strategic sectors, the gains in productivity boost GDP to 2.4% and result in addition employment (green jobs) of 3.5% above the base path by 2030. As noted in scenario 1, a CO2 tax without some kind of offsetting productivity and energy efficiency improvement has notable negative effects on GDP growth. This highlights again the importance of exploring these issues in greater depth than was possible in this analysis in order to provide advice on tradeoffs based on greater analytical realism.)
  

• 
  Both solid waste (from households and industry) and wastewater are reduced by half from baseline levels. In addition, significant emission reduction is achieved (30% reduction in PM10 and 25% reduction CO2 emissions by 2030.
  

• 
  Also, CO2 intensities per $GDP decline below the base path trajectory. Under urban greening with taxation and jobs-financing expenditures, CO2 intensity is reduced to 0.63 kg/$GDP, and is further reduced with the assumed opportunities for strategic innovation to 0.44 kg/$GDP by 2030, on a par with the OECD average (again indicating the importance of further refining this aspect of the analysis).
  

• 
  The total revenue of the urban greening policy reaches 3.6% of GDP by 2030.
  

• 
  Environmental taxes/fees amount to the following:
  

• 
  0.52% of GDP for industry and 0.55% of GDP for households in the short-term (2015), which falls to 0.20%, and 0.10%, respectively, by 2030;
  
• 
  0.17% of GDP for PM10, rising to 0.66% of GDP by 2030
  
• 
  0.18% of GDP for CO2, rising to 0.7% of GDP by 2030
  

• 
  The marginal cost of CO2 emissions abatement (MAC) reaches $62/ton by 2020 then falls to $52/ton by 2030. As noted, this tax is set to meet quantitative emissions goals established under the EU Directive and the NCAC. On the other hand, the resulting marginal cost in 2020, , is quite a bit higher than the numbers often encountered in the policy literature (and the lower figure for 2030 reflects that this is the model’s end date versus more and tougher restrictions to be met in the further future).
  

• 
  At the sectoral level, key impacts include the following (by 2030):
  

• 
  Higher than average CO2 emissions reduction (30% compared to 25%);
  
• 
  Iron and Steel, among the most pollution-intensive sectors, achieves a reduction in PM10 and CO2 emissions by almost 60% over the base path;
  
• 
  Solid waste abatement reaches EU directives requirements;
  
• 
  Electronics, Construction, and Automotive expand by 15%, 7%, and 9% respectively, leading to gains in employment, while Machinery and White Goods remain almost on par with their base path trajectory;
  
• 
  Iron and Steel and Electricity sectors contract. For Iron and Steel, this is due to the burden of taxation, and the sector’s structural dependence on coal as an intermediate input; and
  
• 
  Export performance of the strategic sectors follows their expansionary outlook. Automotive, with an expansion of 11% over the base path in 2030, becomes the leading sector, increasing its share in aggregate exports (including services) to 15%. Electronics exports are observed to expand by 22%, and also constitute a major export driver.
  

Figure 6.4 CO2 intensities

Figure 6.5 PM10 intensities

Figure 6.3 Summary of policy scenarios for greening the urban economy

While greening the rural economy requires focusing on broader natural resource management issues including biodiversity conservation, forestry, and water resources, the focus of the present study is more modest and covers mainly agriculture, not only because of its socio-economic importance, but also because of its environmental footprint in terms of water use, agro-chemicals, and soil degradation. (see agriculture section above).

The main issues considered, and places where greening measures could augment the broader sector policies implemented by the Government, include; (i) the lower levels of productivity and land degradation in rainfed agriculture; (ii) the overuse of pasture resources and its impact on livestock productivity; (iii) the low levels of efficiency of water use for irrigation; and (iv) the externalities related to production intensification (agro-chemicals and salinization).

Given the issues discussed in section 5.1 above, in addition the low productivity levels and significant land degradation in rainfed and pasture areas, and the fact that agriculture uses about 74% of the country’s water resources for irrigation--in the context of growing water scarcity, increasing demand by other sectors and uses, and the looming problem of reductions in base flow due to climate change in the future--and given that the Government plans to further develop an additional 3 to 4 million hectares of irrigation by 2030 (e.g., the GAP project and other developments), as well as the status of irrigation technology, lack of volumetric pricing, intensive use of fertilizer and pesticides, as well as poor drainage infrastructure (leading to yield-reducing salinization problems), the of greening policies we chose to investigate in this study are aimed at a “triple-win:” more efficient use of land and irrigation water, reduced land degradation and improved soil carbon (mitigation), and enhanced productivity together with increased resilience to future climate change (adaptation).

The following three specific greening measures were modeled and evaluated

1. 
   Adoption of Conservation Agriculture/no-till (including minimal soil disturbance, proper management of crop residues, and crop rotation) in an area of 5 million ha which is currently traditionally tilled;
   
2. 
   Rehabilitation of 5 million ha of degraded pastures; and
   
3. 
   Irrigation efficiency improvements in the 5.2 million ha currently irrigated plus the 3.3 million ha of irrigation schemes yet to be developed.
   

For each of these greening measures, both costs (i.e., investments) and benefits have been estimated. Benefits have been distinguished between “on-site” (such as increased farm productivity and improved pasture), and “off-site” (such as the benefits derived from reduced sedimentation and its impact on downstream infrastructure and water quality). The estimated Net Present Value over 2014-2030 of adopting the greening measure on a large scale, as indicated above, tops US$11 billion (Table 6.4). Key assumptions and caveats underlying these estimates are detailed in the background note on agriculture sector. The economy-wide impacts of these greening measures were evaluated through the CGE model.

Greening the rural economy through sustainable agriculture is done with a set of three complementary scenarios:

1. 
   Scenario 1: Improving water use efficiency in irrigated agriculture (labeled EXP_AG01): Water irrigation reduced to two-thirds of the base path utilization. This is done through the introduction of a marginal water fee determined endogenously by the model and representing the shadow price of the binding water availability for irrigation.
   

2. 
   Scenario 2: Improving water use efficiency in irrigated agriculture and earmarking revenues from water fees for improved irrigation technology (labeled EXP_AG02): While under the first scenario above, the revenues from the water fee accrue directly to public revenues with no further earmarking, under this scenario, irrigation water fee revenues are used to sustain further R&D and extension services to improve productivity (crop yields) in the rural economy. A similar approach to modeling the process of revenue use for innovation was done in the case of urban greening policies, above, is also used here (Box 6.5).
   

3. 
   Scenario 3: Improving water use efficiency in irrigated agriculture, earmarking revenues from water fees for improved irrigation technology, and introducing conservation tillage and improved pasture management (labeled EXP_AG03). This scenario introduces greening measures aimed at pasture land improvement and conservation tillage in rainfed agriculture. Of the Turkey’s total pasture land (around 15 million ha in), it is estimated that about 5-7 million ha are severely eroded. Estimates suggest that improved pasture management will likely result in a 30% gain in value-added dry matter yield production-- assumed to equate to about a 30% increase in value-added livestock. Complemented with improved control of soil erosion and switching to conservation tillage practices in agriculture, the annualized gain in net present value terms is estimated to reach 3.67 (billion TL) (2.1 billion $). This gain reaches 1.8% of the real agricultural output supply in 2015, and amounts to 1.02% of its real value by 2030. This is reflected in the CGE model as an exogenous increase in the rate of productivity growth in agricultural output by 0.02% per annum starting in 2015.⁵² To further capture the effects of improved land quality through mitigating soil erosion from poor pasture management, the available supply of rainfed land increases by 1.5 million ha annually starting 2011 until 2015.
   

Box 6.5 Generation productivity enhancements through earmarking revenue from irrigation water fee for R&D/innovation in irrigated agriculture

As part of the new environmental component of the CGE model, a two-step approach is used to model the productivity gain from innovation stemming from earmarking irrigation water fee revenues.

Step 1: innovation-driven productivity gains are modeled as:

₍₆₎

Given the positive yield gains, the agricultural productivity coefficients are updated via

(7)

Note: The innovation functions are adapted from earlier application by de Melo and Robinson (1992) for the case of generating productivity gains from trade externalities.

Source: CGE model and analysis (Annex2)

• 
  A marginal value of water resources of 28 cents TL/m3 ($ cents 16). The corresponding marginal abatement cost curve indicates a rate of $55 per ha of irrigated land in 2011, increasing gradually to $60/ha by 2030, noting that the current irrigation water charge is in the range of $100 to $200 per hectare (as there is no volumetric system of charges).
  
• 
  Potential revenues generated from the higher water fees would represent 0.1% of GDP and 0.62% of the value of agricultural output.
  
• 
  But GDP would be 0.35% lower than the base path upon impact in 2011, and it would be 0.4% lower in comparison to the 2030 base path level (Figure 6.6). Again, the 2030 results reflect a slower rate of GDP growth with greening, but not an absolute decline.
  

• 
  An increase of GDP by 1.8% over its base path value by 2030 (in real terms).
  
• 
  Productivity gains of 0.4% in 2011 gradually increasing to 0.95% by 2030.
  
• 
  These expansionary effects from the productivity gains emanating from translating fee revenues into research and extension lead to a further increase in the marginal value of water resources to 32 cents TL/m3 ($ cents 18) with a corresponding marginal cost of water irrigation 60$/ha by 2030.
  

• 
  An estimated gain of 3.6% in GDP by 2030.
  
• 
  Expansion of the rain-fed land (to simulate conservation tillage and improved pasture management) induces important substitution effects reducing the burden of the water fee and leading to a reduction of the marginal cost of irrigation water starting in 2015 ( lowering to 38 $/ha by 2030).
  
• 
  Potential revenues generated from the higher water fees would represent 0.06% of GDP and 0.36% of the value of agricultural output.
  
• 
  The policy is employment-neutral in the sense that less than 20,000 rural jobs are added by 2030. This is to be expected since some measures, like conservation tillage, tend to increase labor use, but others, like improved irrigation technology, would have the opposite impact.
  

• 
  Internalizing environmental externalities by imposing pollution taxes on industrial and household solid waste and water discharges, PM10 and CO2e emissions;
  
• 
  Earmarking (part of) the revenue from pollution taxes for financing green jobs for otherwise unemployed workers at the ongoing urban wage rate;
  
• 
  Earmarking the carbon tax revenues for improved R&D and innovations for the strategic industrial sectors (as defined within the Industrialization Strategy Document, 2011);
  
• 
  Introducing “use efficiency” fees on irrigation water for cost recovery;
  
• 
  Earmarking the irrigation fees for rural R&D and innovation to boost agricultural productivity growth; and
  
• 
  Improving pasture land and soil erosion control and introducing conservation tillage practices.
  

• 
  GDP increases in real terms to 3,186 billion TL in 2030 (in fixed 2010 prices), 5.8% higher than the base path (again, itself reflecting economic growth over the periods);
  
• 
  Consumption contracts slightly as a result of fiscal greening measures (67% of the GDP in 2030 compared to 68% in the base run) ;
  
• 
  Investment is doubled between 2015 and 2030;
  
• 
  There is no major impact on trade balance (trade deficit around 12 billion TL in fixed 2010 prices in the base run and combined greening scenarios)
  
• 
  Innovation leads to Automotive and Electronics becoming the leading sectors of growth, significant gains are also achieved in Machinery and Construction;
  
• 
  In comprehensive greening scenarios (referring to urban and rural greening) total employment rises to 26.7 million workers (Table 6.6) (5.3% above the base path, slightly lower than the increase in GDP over the base path), while sectoral results reveal that employment gains are strong in Automotive (59.3%); Electronics (34.7%); and Machinery & White Goods (11.7%);
  
• 
  Solid waste both in industry and the household sector meet the EU-inspired coverage and management standards, and water pollution (wastewater) is reduced by half;
  
• 
  Aggregate emissions of PM10 is cut by 25% (meeting WHO standards), and gaseous emissions as measured by CO2e is reduced by 21.3% in 2030;
  
• 
  The intensity of CO2 emissions per $GDP is observed to fall to 0.44 kg/$GDP –the level of the OECD average for 2008.
  

It has been noted previously that the model uses very simplified approaches to represent complex processes of substitution, alternative technology adoption, and innovation. The additional importance of how macroeconomic growth policies interact with environmental policies cannot be over-estimated. In this study, concurrent applications of more macro-oriented measures to stimulate overall TFP growth, and increase jobs through public expenditure, serve to offset the sector-and-pollutant-specific reductions in the rate of GDP growth. The net positive impacts on GDP growth in various scenarios do not, however, reflect a win-win in the sense that the environmental measures are somehow uniquely responsible for the growth benefits. The health-related productivity improvements and (arguably) the specific application of CO2 tax revenues for (induced) energy efficiency improvement do reflect win-win outcomes, and the specific funding of green jobs in the water and solid waste sectors reduces the economic burden imposed by the waste taxes in the model. However, the other benefits depend on a source of expenditure, not that it is specifically environmental tax revenue. For example, the general increases in total factor productivity taken as being induced by application of CO2 tax revenues to unspecified innovation investments could be as easily achieved in the model with no environmental policies, simply by channeling some other sources of revenues to these activities. Similarly, unemployment reduction could be induced through taxes or subsidies on job creation that are not tied to environmental revenues or activities, as well as through labor market reforms. While pursuit of green policy goals adds to the value of also utilizing other macroeconomic-level growth policies to mitigate the cost and thus increase the net social benefit of green measures, solid macroeconomic policies remain a priority in their own right.