Hélène Maisonnave
email: helene.maisonnave@ecn.ulaval.ca

Ramos Mabugu

Margaret Chitiga

Véronique Robichaud

Mai / May

Analysing Job Creation Effects of Scaling Up Infrastructure Spending in South Africa

Keywords: Employment, dynamic CGE model, infrastructure scale up, externalities, South Africa JEL Classification: D58, D92, H54, H59 Coal mining 0, 09 0, 11 0, 05 0, 03 0, 01 0, 00 0, 14 0, 08 0, 13 0, 04 0, 33

In a first for South Africa, we draw on literature on infrastructure productivity to model dynamic economywide employment impacts of infrastructure investment funded with different fiscal tools. According to the South African investment plan, the policy will affect the stock of infrastructure as well as the stock of capital of some private and public sectors. Increased government deficit financed infrastructure spending improves GDP and reduces unemployment. However, in the long term, the policy reduces investment and it is not sustainable for South Africa to let its deficit grow unabated. Increased investment spending financed by tax increases has contrasting implications on unemployment. In the long run, unemployment decreases for all types of workers under one of the scenarios. In the short run, only elementary occupation workers benefit from a decrease in unemployment; for the rest, unemployment rises. Findings have immediate policy implications in various policy modelling areas.

Introduction

The literature on the causes of economic growth5 presents evidence that infrastructure and capital formation are important determinants of economic growth and rising per capita incomes over time. This is a lesson that has been well learned and applied in Asian economies over the last several decades where large public investments have contributed to high economic growth. According to [START_REF] Estache | Infrastructure and Development: A Survey of Recent and Upcoming Issues[END_REF], infrastructure seems to be returning to the agenda of development economists. In South Africa, investment in infrastructure in the years preceding democracy was in general very low. During the era of Growth, Employment and Redistribution (GEAR) from 1996 to 2002, public infrastructure investment fell from 8.1% to 2.6% of gross domestic product (GDP). The emphasis during that time was fiscal discipline more than expenditure increase. It was from the Accelerated and Shared Growth for South Africa (AsgiSA) plan in 2002 that a drive for infrastructure was couched explicitly in policy. Today, the main pillars of government economic policy, the New Growth Path (NGP), the Industrial Policy Action Plan and the National Development Plan (NDP), are anchored in a significant ramping up of current and capital expenditure by the state. The government and state-owned companies plan to spend about R845bn on infrastructure over the next three years, which they expect will contribute significantly to meeting the government job-creation target of 5-million jobs in 2020 (NGP) or 11 million jobs by 2030 (NDP). So much is riding on this state infrastructure spending as the solution to reducing poverty, inequality and unemployment and generating economic growth.

The question whether there are economic gains from the provision of higher levels of public spending on capital is fundamental 6 . If a higher level of capital raises the growth path of the economy then it is justifiable on both equity and efficiency grounds. Whilst no one will argue about the equity issues involved, some will no doubt argue that additional public spending can create efficiency costs. There are a number of possible reasons for this. Firstly, whilst public capital is usually productive, this is by no means the consensus view empirically, and the literature contains a wide variety of estimates of the size of the marginal product of public capital ranging from positive to negative. Even if it is assumed that the marginal product of additional public spending is positive, critics might presumably ask further questions. First, is the effect of such spending permanent or temporary, and if temporary, of what magnitude and after what period of time can one expect positive effects? Government spending on public sector capital may have positive multiplier effects and may, therefore, raise economic activity and thus economic growth. However, once installed will these effects drop to zero? The answer here is not clear. In a Solow -type growth model the effects on growth would be expected to be transitory, positive initially, but zero in the new steady state with a higher level of output. But if public capital raises education and innovation, which might be expected in South Africa, the 3 effects could be permanent and indeed much of the gains could come from spillover effects raising the productivity of private sector capital and labour. Secondly, critics of public spending would presumably argue that even if public capital has a positive effect, its magnitude would need to be compared to the productivity of private sector capital, if inefficient public capital spending is crowding out efficient private sector capital the effects on the economy could be negative. Thirdly, consideration would have to be given to how the public spending is financed. Raising taxes or borrowing could both have negative effects on economic activity which might offset the gains of public sector capital spending. This paper reflects on the current state and likely future of South African infrastructure investment policy, focusing specifically on government infrastructure spending and how alternative financing arrangements will affect employment, both in the short and longer-term. For these purposes, a recursive dynamic computable general equilibrium (CGE) model with elaborate labour market disaggregation, government budget constraints and alternative funding options for infrastructure scale up is used. The rest of the paper is structured as follows. Section 2 reviews the literature to situate the study, followed by a presentation of the model and data in section 3. Section 4 presents the simulations and implications of introducing alternative infrastructure investment in such a framework. We close the paper with concluding remarks and policy recommendations in section 5.

Literature Review

(Neo) classical economics generally assumes that activist fiscal policy is unnecessary to increase employment and production. Government expenditure is generally believed to be consumptive and leading to crowd out of private investment if financed with public debt. Wagner's law assumes that public expenditure is endogenous and hence cannot be used as a policy lever. Politicians at best should pursue balanced budget strategies. Keynesian economists on the other hand believe that public expenditure is important in determining the level of income as well as its distribution. The market mechanism would not be sufficient to restore full employment. There is a substantial body of empirical literature related to the public expenditure-economic growth nexus (see [START_REF] Moreno-Dodson | On the Marriage between Public Spending and Growth: What Else Do We Know?[END_REF] for a review of government spending and economic growth studies). An important strand of the literature of direct relevance for this study is the idea that the composition of public expenditures (capital versus current) can have differential impacts on economic growth.

There is an extensive literature, both theoretical and empirical, on the effects of public capital spending on output dating back to [START_REF] Arrow | Public Investment, the Rate of Return and Optimal Fiscal Policy[END_REF] and [START_REF] Aschauer | Is Public Expenditure Productive[END_REF]. During the 1980s and 1990s, there was strong academic interest (particularly in the United States of America), on the link between public investment in infrastructure and economic growth. From the outset, it is interesting to note the trend in which this research has followed, from the initial headline estimates of a production elasticity of 0.4 in 1989 to the more modest assessments of 0.1 in 1997. The link between infrastructure investment and economic growth has been a major topic for academics since the publication of Aschauer (1989)'s seminal paper which found that public investment in infrastructure was a very important source of economic growth. [START_REF] Aschauer | Is Public Expenditure Productive[END_REF] considered the relationship between aggregate output and the stock and flow of government spending variables and concluded that 'core' infrastructure of streets, highways, airports and mass transit systems should be given more weight when assessing the role government plays in the promotion of economic growth and productivity improvements. [START_REF] Aschauer | Is Public Expenditure Productive[END_REF]'s work suggested that the elasticity of output with respect to government capital was highly positive, within a range of 0.38 to 0.56. This implies extremely high returns, with the marginal product of government capital in the region of 100 per cent per annum or more. This would imply that one unit of Government capital pays for itself in terms of higher output in a year or less. Given these results, it's not surprising that Aschauer (1989)'s work was to initiate the 'public infrastructure debate' which has resulted in numerous academic studies since. [START_REF] Munnell | Policy Watch: Infrastructure Investment and Economic Growth[END_REF] provides an excellent assessment of the early literature on the public infrastructure debate. She shows that the main problem with Aschauer (1989)'s work is that his results do not rule out the possibility that the direction of causality runs from growth to infrastructure (i.e. economic growth might lead to an increase in the need for investment and/or an increase in the availability of funding), or that the correlations that he found are spurious. Nevertheless, in response to the critics who claim that the wide range of estimates of public capital's impact on output 'make the empirical linkages fragile', [START_REF] Munnell | Policy Watch: Infrastructure Investment and Economic Growth[END_REF] provides evidence to suggest these claims are misleading. As illustrated in Table 1 below, in almost all cases the impact of public capital on private output has been found to be positive and statistically significant. Munnell (1992, p194), [START_REF] Munnell | Policy Watch: Infrastructure Investment and Economic Growth[END_REF] concludes that the evidence suggests that, in addition to providing an immediate demand-side economic stimulus, public infrastructure investment has a significant, positive effect on output and growth. However, she stresses that in a policy making context 'Aggregate results cannot be used to guide actual investment spending. Only cost-benefit studies can determine which projects should be implemented' (Munnell (1992: p196)). [START_REF] Gramlich | Infrastructure Investment: A Review Essay[END_REF]'s influential paper also unpacks many of the arguments and assertions made by [START_REF] Aschauer | Is Public Expenditure Productive[END_REF], along with the mass of academic literature which followed. [START_REF] Gramlich | Infrastructure Investment: A Review Essay[END_REF] begins his paper by using the narrow public sector ownership definition as the stock of infrastructure capital -but highlights that a wider meaning could involve private infrastructure capital, human capital investment and research and development spending. This emphasises the importance of definition -what type of investment is being classified as infrastructure and what type is then being linked to economic growth. [START_REF] Gramlich | Infrastructure Investment: A Review Essay[END_REF] notes that projects such as a new highway might provide a very high return, whereas maintenance of rural roads might provide low or even negative economic rates of return; in such areas, investment objectives may be primarily social rather than economic. He applies this by showing that only two-thirds of the capital stock analysed by [START_REF] Aschauer | Is Public Expenditure Productive[END_REF] even purports to raising national output -and to varying degrees -making his claims about the major positive influence of infrastructure on economic growth less plausible.

As research in the field progressed, disputes over the direction of causality between changes in productivity and investment in infrastructure became dominant. [START_REF] Evans | Is government capital productive? Evidence from a panel of seven countries[END_REF] analyzed infrastructure and productivity data for seven Organisation for Economic Co-operation and Development (OECD) countries between 1963 and 1988. The study found strong correlations between the two variables, but concluded that the direction of causality was the opposite of that reported by [START_REF] Aschauer | Is Public Expenditure Productive[END_REF] and [START_REF] Munnell | Policy Watch: Infrastructure Investment and Economic Growth[END_REF]. That is, increased stocks of public capital were the result of increased productivity and economic growth, not the cause. In analysing the correlation between average GDP and government net capital stock, they concluded that "there is no evidence that government capital is highly productive" (Evans and Karras (1994: p278). [START_REF] Zegeye | U.S. Public Infrastructure and Its Contribution to Private Sector Productivity[END_REF] supports the [START_REF] Evans | Is government capital productive? Evidence from a panel of seven countries[END_REF] study, concluding that infrastructure is a normal good, where wealthy counties will tend to have more and poor counties less. [START_REF] Zegeye | U.S. Public Infrastructure and Its Contribution to Private Sector Productivity[END_REF]'s report found the output elasticity between public infrastructure and private investment was just 0.02.

Several other authors have attempted to resolve the question of causality, refining their methodologies to ensure they capture the results of infrastructure investments, and not the results of economic growth. A 2000 OECD study by Demetriades and Mamuneas, and a 2003 study by Esfahani and Ramirez handled the causality issue by introducing a "time-lag" between variables for public infrastructure and productivity. In these studies, investments were compared with the productivity data several years afterwards, allowing time for the benefits of infrastructure investments to manifest themselves in the productivity data, and reducing the chance of misrepresentation of economic growth impacts as productivity impacts. Both studies using this technique found that public infrastructure does have a measurable impact on increasing productivity and economic growth, although not of the magnitude reported by [START_REF] Aschauer | Is Public Expenditure Productive[END_REF]. [START_REF] Lau | Public Infrastructure and Economic Growth: Time Series Properties and Evidence[END_REF] published an important econometric paper on public infrastructure and economic growth. This was subsequently referred to as being 'the most sophisticated subsequent econometric studies' by [START_REF] Sactra | Transport and the Economy, United Kingdom[END_REF] and commended for taking the research some way to circumventing the 'causality' and 'definition' difficulties highlighted by [START_REF] Munnell | Policy Watch: Infrastructure Investment and Economic Growth[END_REF] and [START_REF] Gramlich | Infrastructure Investment: A Review Essay[END_REF]amongst others. The authors estimate the elasticity of output with respect to public capital to be 0.11. Although this would imply a much lower marginal product of public investment than that indicated by Aschauer (1989)'s original paper, it still suggests that infrastructure investment has a significant impact on output.

The South African literature on the impact of infrastructure investment on economic growth is still small and relatively recent. It has followed along a similar path to the trends observed for the international literature. A good account of the literature is available in [START_REF] Fourie | Some policy proposals for future infrastructure investment in South Africa[END_REF]. Table 2 summarises all the studies that we are aware of on the topic. The early studies have relied on classical econometric tools while the latter studies have used more recent techniques of Vector Error Correction Models (VECMs) and Vector Autoregressions (VARs). In spite of differences in methodology, the studies report a positive output elasticity. [START_REF] Bogetic | Infrastructure and Growth in South Africa: Benchmarking, Productivity and Investment Needs[END_REF] find positive effects of infrastructure on labour productivity but negative effects on total factor productivity. Their explanation for this counterintuitive result is that infrastructure only has direct effects and no indirect effects! This is grossly at odds with predictions from received theory where indirect effects are most important. In follow up work, [START_REF] Fedderke | Infrastructure and Growth in South Africa: Direct and Indirect Productivity Impacts of 19 Infrastructure Measures[END_REF] concluded that infrastructure investment had a positive impact on productivity: total factor productivity increased by 0.04% when investment in economic infrastructure increased by 1%. However, [START_REF] Fedderke | Infrastructure development and economic growth in South Africa: a review of the accumulated evidence[END_REF] suggested that the AsgiSA infrastructure plan might have had unfavourable effects in South Africa. [START_REF] Fourie | Some policy proposals for future infrastructure investment in South Africa[END_REF] finds bi-directional causality between infrastructure and growth and also finds large positive returns to infrastructure on equity. Thus, the South African econometric studies show favourable effects of infrastructure spending on growth, irrespective of the methodology used. Some even go further to argue that infrastructure on equity has higher returns than economic infrastructure 7 .

Compared to the econometrics literature, the literature on CGE applications of public capital expenditures and links to economic growth is more recent and still growing. Similar to the econometrics literature just reviewed, the findings of this literature are mixed. Whilst most find that the output elasticity of public expenditure is positive, the magnitudes of the effects vary considerably. In a summary of some of the main studies on infrastructure, Kirsten and Davies (2008) show that, in general, studies that looked at various infrastructure sectors (roads, sanitation, electrification and dams) display varied results -some are beneficial for poverty reduction, others actually cause poverty. Using a static CGE model, [START_REF] Perrault | The impact of infrastructure spending in Sub-Saharan Africa: A CGE modelling approach[END_REF] explore the impact of scaling up infrastructure in six African countries with different economic structures. They find that the different economic structures lead to differences in impact of investment funded by the same sources with the same model. The analysis shows the importance of the underlying economic structure in determining the impact of infrastructure expenditure in a country. This suggests that the structure of the economy where these policies will be applied needs to be taken into account.

Another strand of related literature concerns itself with the effects of scaling up aid to developing countries. Received wisdom based on standard analysis came to the conclusion that scaling up aid flows would generate sustained growth and improved standard of living [START_REF] Adam | Exogenous Inflows and Real Exchange Rates; Theoretical Quirk or Empirical Reality? An IMF Paper[END_REF]). This view has been challenged by authors who point out that both intended and unintended consequences discussed largely under the rubric of what has come to be referred to as 7 [START_REF] Ayogu | Infrastructure and Economic Development in Africa: A Review[END_REF] also surveys the theoretical literature on infrastructure and growth and then reviews the empirical evidence globally and within the African region. Overall he concludes that the question is not whether infrastructure matters but precisely how much it matters in different contexts? Ultimately, this is an empirical question that the literature has not yet resolved satisfactorily. In contrast, according to him, the crucial issue-understanding policymaking processes in infrastructure-remains little understood and largely under-researched.

'absorptive capacity constraints'(see for example [START_REF] Burnside | Aid, Polices and Growth, Revisiting the Evidence[END_REF]; [START_REF] Clemens | The Millenium Challenge Account: How much is too much, how long is long enough?[END_REF]; [START_REF] Heller | Pity the Finance Minister: managing a substantial scaling up of aid flows[END_REF];Allen (2005)) make the impact of aid on economic growth indeterminate. A major concern in this respect is the so-called Dutch disease effect associated with scaling up foreign aid8 . Recent evidence (including [START_REF] Adam | Aid, Public Expenditure and Dutch Disease" Centre for the Study of African Economies[END_REF]; [START_REF] Allen | The Macroeconomics of Managing Increased Aid Inflows: Experiences of Low-income Countries and Policy Implications[END_REF]; [START_REF] Heller | Pity the Finance Minister: managing a substantial scaling up of aid flows[END_REF]; [START_REF] Bourguignon | Constraints to Achieving the MDGs with Scaled-Up Aid[END_REF]) has shown that the conventional Dutch disease effects may be overturned if there are productivity spillovers in both tradable and nontradable sectors. Using Uganda as an example, [START_REF] Adam | Aid and the Supply Side: Public Investment, Export Performance and Dutch Disease in Low Income Countries[END_REF] construct an aggregated CGE model and demonstrate that Dutch disease type effects can be avoided if the non-tradable sectors benefit from infrastructure investment externalities. [START_REF] Savard | Scaling Up Infrastructure Spending in the Phillipines: A CGE Top Down Bottom Up Microsimulation Approach[END_REF] extends this kind of reasoning in three ways, namely, dropping the tradable-nontradables dichotomy, allowing for a wider variety of funding options for infrastructure spending and introducing a top-down bottom up microsimulation module to allow for poverty analysis. Applying the methodology to explore the impact of scaling up infrastructure in the Philippines, [START_REF] Savard | Scaling Up Infrastructure Spending in the Phillipines: A CGE Top Down Bottom Up Microsimulation Approach[END_REF] finds that the macro results obtained from the analysis are similar to [START_REF] Adam | Aid and the Supply Side: Public Investment, Export Performance and Dutch Disease in Low Income Countries[END_REF] and Estache (2008), although the Dutch disease effects disappear when they assume the presence of production externalities. There are also no major differences at the macro level when taking into account funding options to scale-up infrastructure. However, poverty analysis shows stark differences and in particular the VAT funding option yields the most favourable outcomes in terms of poverty reduction when compared to the foreign aid option and the income tax option. To improve the analysis on this front, [START_REF] Savard | Scaling Up Infrastructure Spending in the Phillipines: A CGE Top Down Bottom Up Microsimulation Approach[END_REF] suggests that a sequentially dynamic framework would be a more appropriate tool.

A number of recent studies have sought to make contributions along this line. For instance, [START_REF] Jung | The impact of public education expenditure on human capital, growth, and poverty in Tanzania and Zambia: A general equilibrium approach[END_REF] use a recursive dynamic CGE framework and showed that infrastructure spending benefited poor people in Tanzania but worsened the plight of the poor in Zambia. A fair amount of authors investigating the impacts or challenges of scaling up aid to achieve the Millennium Development Goals (MDG) (see for example [START_REF] Bourguignon | Constraints to Achieving the MDGs with Scaled-Up Aid[END_REF], [START_REF] Hailu | Scaling-up HIV and AIDS Financing and the Role of Macroeconomic Policies in Kenya[END_REF]Serieuxet al.(2008)) have also used this recursive dynamic approach. The model that is typically used in these exercises is referred to as the MAMS model (for details on the MAMS model, see [START_REF] Lofgren | An Ethiopian Strategy for Achieving the Millennium Development Goals: Simulations with MAMS model, Processed[END_REF]). This model extends static standard CGE models of the type discussed above in two key respects. First is the incorporation of recursive dynamics and second is the addition of an MDG module that endogenizes MDG outcomes. The paper by Bourguinon and Sundberg (2006) based on the MAMS model calibrated to Ethiopian data concludes among other things, that the impact of large aid inflows on the Dutch disease can be serious but strategic investments to boost productivity and address trade constraints are important in addressing the adverse effects. World Bank (2005) report a similar finding for Ethiopia based on a model that focused on aid-financed investments in human capital. [START_REF] Mabugu | Impact of Fiscal Policy in an Intertemporal CGE Model for South Africa[END_REF] use an intertemporal CGE model to investigate the consequences of an expansive fiscal policy designed to accelerate economic growth in South Africa. The model is oriented towards constraints government faces in financing its expenditures and explains why it takes into account the different sources of income of the South African government, its expenditures and its deficit as well as intertemporal dynamics. The labour market faces a lot of rigidities in South Africa that the intertemporal model does not capture.

Our paper is fundamentally similar in spirit and conception to these CGE-based simulation models just described but applied to reflect the structural features of the current South African economy. Presumably, the extent to which productivity spillovers from infrastructure investments can potentially affect the economy will depend on the particular circumstances of the country. In this respect we draw from the extensive infrastructure productivity econometrics literature discussed to postulate positive productive externalities associated with new infrastructure for South Africa. Unlike [START_REF] Mabugu | Impact of Fiscal Policy in an Intertemporal CGE Model for South Africa[END_REF], labour market peculiarities of the South African economy have been included in our modelling and dynamics are modelled as recursive rather than intertemporal. This paper is intended to contribute to the discussion by providing evidence from South Africa using the economy-wide dynamic CGE model calibrated to contemporary conditions in the country.

Data and Methodology 9

The original Social Accounting Matrix (SAM) used is from Quantec for 2005. The different occupations in the SAM are identified as skilled, semi-skilled and unskilled. For the purpose of this paper, the labour factor is disaggregated further into occupations. Integrated economic accounts from Statistics South Africa (StatsSA) for 2005, where the labour force is split according to occupation and population groups, are used after ensuring concordance with the SAM economic activities codes (Table 3) Agriculture, forestry & fishing 0,09 0,03 0,02 0,05 0,03 0,23 0,07 0,07 0,21 0,10 0,11

Catering & accommodation services 0,23 0,04 0,05 0,10 0,25 0,01 0,04 0,03 0,09 0,03 0,12 Transports services 0,08 0,03 0,03 0,11 0,07 0,00 0,07 0,23 0,09 0,04 0,25 Communication 0,23 0,12 0,09 0,19 0,07 0,01 0,04 0,06 0,04 0,01 0,14 Finance and insurance 0,27 0,20 0,11 0,25 0,05 0,00 0,02 0,01 0,01 0,01 0,09 Business services 0,18 0,26 0,08 0,09 0,15 0,01 0,05 0,03 0,02 0,02 0,11 Other services 0,04 0,09 0,02 0,06 0,06 0,01 0,05 0,03 0,02 0,08 0,53 Public services 0,17 0,12 0,06 0,15 0,33 0,01 0,04 0,03 0,02 0,02 0,05

Source: Authors' calculations from the SAM 13

From Table 4, we can point out that the construction sector is likely to be disproportionately affected by an infrastructure investment program policy because the sector is quite intensive in low skilled workers, especially the ones identified as elementary or unspecified occupations. Moreover, legislators (LEG) represent 15% of the labour force in this particular sector.

For modelling, [START_REF] Gibson | Armington elasticities for South Africa: long-and short-run industry level estimates, trade and industrial policy strategies[END_REF] is used for the trade parameters and low-bound export supply, while demand elasticities are obtained from [START_REF] Behar | Estimating elasticities of demand and supply for South African manufactured exports using a vector error correction model[END_REF]. Estimates for parameters in industry production and household demand are not available for South Africa. Therefore, the study borrows these values from the literature surveyed by [START_REF] Annabi | Functional forms and parameterization of CGE models, PEP[END_REF]. Finally, unemployment rates are drawn from the labour force survey report by StatsSA (2009).

To evaluate the impacts of government's policies in the long run, the dynamic Poverty and Economic Policy (PEP 1-t) standard model by [START_REF] Decaluwé | PEP-1-t. Standard PEP model: single-country, recursive dynamic version[END_REF] is used. However, several assumptions of this standard model are changed in order to take into account the South African economy. The model has two production factors: capital and labour. Labour is disaggregated into three broad types: unskilled, semi-skilled and skilled workers. Each type of broad labour is then disaggregated into occupations. Each activity uses both production factors.

In line with the SAM, the model has 25 activities and 54 commodities. The production function technology is assumed to be of constant returns to scale and is presented in a four-level production process. At the first level, output is a Leontief input-output of value added and intermediate consumption. At the second level, a CES function is used to represent the substitution between a composite labour and capital. At the third level, composite labour demand is also a CES function between skilled, semi-skilled and unskilled labour. Then, the skilled demand is a CES with a low elasticity between legislators, professionals and technicians, capturing the fact that (for instance) it is quite difficult for the firms to substitute a lawyer for a doctor. The semi-skilled demand is a CES with an intermediate value of elasticity between its five components, while the unskilled demand is a CES with a high substitution value, assuming that the producer can relatively easily substitute low skilled workers among them. Figure 1 gives the value-added structure.

Figure 1: The value-added structure

South Africa has high unemployment problems, notably for semi-skilled and unskilled labour. Moreover, unions are very strong in the country. The trade union movement is the most disciplined and the largest in Africa and has influenced labour and other related industrial policies. Unions negotiate salaries and wages, conditions of service, workforce restructuring and retrenchments on behalf of their members. As a result, wages and salaries are rigid, which the model takes into account by assuming a binding minimum wage for each type of worker. Thus, if the production decreases, producers will not be able to decrease their employees' salary below the minimum wage. This rigidity will also have an impact on unemployment, as if producers cannot decrease the wage bill, they will have to retrench some workers.

Following the literature review in the previous section, we introduce a productivity factor to investment in infrastructure. As mentioned, the value added for each sector is a CES composite of labour and capital. We add a productivity factor related to the stock of infrastructure in the country to the function. Modelled in this way, investment in infrastructure will increase the stock of infrastructure capital (𝐾𝐷 𝑡 𝐼𝑁𝐹 ), in the following year. If no investment is made, then the stock of infrastructure capital remains the same and there is no extra increase in value added of a given sector. The value of elasticity 𝜎 𝑗 𝐼𝑁𝐹 is obtained from the existing literature.

As closure rules, we choose the nominal exchange rate as the numeraire in the model. 10Following the assumption that South Africa is a small country, world prices are fixed. However, also assumed is the fact that South African exporters face less than infinite foreign demand for exports: to increase their market share on the world market, they need to reduce their free-onboard (FOB)export prices, increasing their competiveness with respect to other suppliers on the international market. Factor supplies are fixed in the first period and then grow, at the population rate for labour force and using an accumulation equation for capital. 11 Transfers between institutions and government's purchases of commodities are fixed at the base year and then grow at the population rate. The assumption is that the rest of the world's savings is a fixed proportion of GDP, which means that South Africa is not allowed to borrow further from the rest of the world.12

Policy Simulations and Results

. This paper analyses the impact of an increase in public investment, following the South African investment plan presented in the table below for the period 2012 up to 2016, and thereafter at the population rate. The simulated investment programme is split into three components (a)

investment in government sectors (e.g. education, justice) that increase the stock of capital of public sectors, (b) investment in infrastructure (e.g. roads, harbours, airports) that does not increase the stock of capital of any sectors in particular and can be considered a public good and (c) investment in productive sectors (e.g. investment in the energy sector) that increase the capital stock of a given sector. Based on the literature reviewed, the simulations thus take into account the effect of infrastructure productivity on the other sectors. Assuming productivity effect of infrastructure investment on other sectors means, for instance, that the construction of a bridge (investment in infrastructure) will have an impact on other sectors if the use of this bridge reduces travel time) or government investment in building a road (infrastructure spending), or in constructing/renovating a harbour, has impacts on other sectors: their transport margins will decrease and they will be able to trade more, using the same quantities of labour and capital.

Government investment can also increase private capital stock, for instance when government invests in a nuclear plant, it increases the stock of capital of the electricity/energy sector. Four different ways of financing these policies are proposed. First, government totally finances the increase (i.e. government's savings are endogenous and, given the policy set up, might decrease). Then, in the next three finance options, government's deficit is kept constant, and the increased spending is financed through increasing direct taxes on households, increasing firms' direct taxes, and increasing indirect taxes. .

1-Deficit financed investment policy

The results of an increase in government's public investment on unemployment are shown in Table 6. The policy has a very positive impact on unemployment for all the different types of workers both in the short run (2012) and long run (2020). Government's activities are more intensive in skilled and semi-skilled workers, and so the impact is greater for these two types of workers. For skilled workers, unemployment disappears in 2015 and for all categories, positive impacts remain after the simulations years. Table 7 presents the impacts on production for each sector of the economy. In the short run, most of the sectors increase their production, but in the long run quite a number of them experience a decrease. The reason why impacts on production are quite positive for most of the sectors is because these activities do not suffer total crowding out effect as some public investment is directly improving their production (as the electricity sector) and all the sectors benefit from a decrease in margins costs, due to the improvement of infrastructure in the economy. The increase in government spending also has an impact on the other sectors through an increase of intermediate demand. To produce more, government sectors need extra public servants, buildings, and all types of commodities produced by the other sectors. With the decrease of unemployment, workers also receive an increase in wages. Indeed, as government's activities need more workers to produce, they will attract skilled and semi-skilled workers mainly by offering a better wage than the other activities. Thus, to keep their workers, the other activities will also have to increase the wages they pay to their workers, which results in increased production costs. Sectors with a similar labour demand structure will find it more costly to produce and this explains why their production levels decline. The decline is also due to a drop in total investment induced by government crowd out. The impacts on agents are quite interesting as they differ. Households benefit from this policy because of the decrease in unemployment and the increase in wages raise household income (Table 8). Note that although their transfer income from firms (dividends) decreases, overall household income increases in the long run by almost 1%. Household savings and consumption also increase, as they are fixed proportions of disposable income. Firms are suffering but less than in the case had investment in infrastructure been assumed to have no productivity effect. The negative impact on firms is less in the short compared to the long run. Capital income decreases, and so do firms' income and savings, because of the drop in total investment (Table 9). Table 10 presents all the sources of government's income and how they react to the increase in government spending. The first component represents transfer income and comes mainly from firms (dividends). The second one represents all the taxes on production (on labour, capital, production). The third one is the sum of all taxes on products (import taxes, VAT, export taxes, excise taxes, fuel levy). The final one is total direct taxes paid by households and firms. Government income is slightly decreasing in the long run, due to the decrease in transfers government receives from firms and the receipts from firms' direct taxes. Not surprisingly, we observe a drop in government's savings as there is no tax policy adjustment to finance the investment program. The drop in government savings, followed by the drop in firms' savings, leads to a decrease in total investment. While a crowding-out effect of investment is evident, the impact on private investment is less harmful because a part of government investment is productive. The impact on GDP is hardly perceptible as shown in Figure 2. With this type of policy, the idea is to see what happens in the long run. It is known that in the short run, government deficit increases a lot, but in the long run, the pressing policy issue is 'can this spending create a greater economic activity in order to generate new revenue'? For instance, a policy that creates jobs will have an impact on the fiscal side, as new workers will get income and pay new taxes (direct and indirect). The next set of simulations address this issue.

2-Tax financed investment policy

The first simulation has very positive results on unemployment and benefits to households. However, in the long term, the drop in total investment tends to reduce economic growth. Moreover, it is not sustainable for South Africa to let its deficit grow unabated. Therefore, the same simulation is presented, but the closure of the model is changed: government's savings are kept fixed, and an endogenous tax finances the policy. In Simulation FinA, the direct tax rate of households adjusts. In Simulation FinB, the direct tax rate on firms adjusts, and in Simulation FinC, the indirect tax rate adjusts. The results of these three simulations are presented together.

In terms of unemployment, as shown in the following tables, the results differ according to the scenario. FinB scenario seems to be the less harmful across all categories of workers. Note that for skilled workers, as the values of unemployment were low at the base year, the percentage change look dramatic. Note though that results are still very negative under Simulation FinC. Indeed, under this scenario, both agents and activities are hit by the increase in commodity tax rate. The impact on the sectors depends on how heavily sectors rely on investment. Activities that face an increase in their input prices (in terms of intermediate consumption) will retrench workers, and reduce their production. The impact is not uniform across sectors. Indeed, some sectors are directly favoured by the investment policy, especially the construction sector. Moreover, some sectors do not directly benefit from the policy, but as they produce investment goods, their production will increase. Once again, results under FinC are very harmful for the economy. The impact on households is negative because the drop in transfers they receive from firms, and the decrease in labour income they receive (Table 15). Note that in the long run household income falls the least under FinA, that is, when direct tax rate adjusts. The impact on firms is also negative, notably under Simulation FinB, as they face an increase in the direct taxes they pay. Here, firms' savings drop by almost 30% in the long run, which will have a massive impact on private investment. In the three scenarios, government income increases due to the fiscal mechanism set up. Private investment decreases and is worse when firms have to finance the policy. This is because firms contribute significantly to private investment. Overall, total investment increases for each scenario. As public investment increases due to the policy, total investment also increases. The increase is less significant under FinB (Figure 3).

Figure 3 : Impact on total investment

Finally, from Figure 4 it can be observed that the policy is less harmful to GDP when financed by firms. Indeed, when households finance the policy, the impact on consumption and thus on GDP is too big. Needless to say, in Simulation FinC, the results are very bad. Financing the policy through an increase in indirect tax penalises the entire economy. This paper has analysed the investment policy the South African government plans to set up under four different fiscal scenarios. The way this investment plan has been treated in our modelling allows the government to intervene in public and private sectors of the economy. The benefits of infrastructure investment are taken into account through a productivity mechanism that will enhance other sectors. Particular attention has been paid to the labour market in the modelling.

Besides improving the quality of infrastructure, the government wishes to reduce unemployment that is endemic in the country. In terms of employment, the results are quite disappointing: indeed, except under the first scenario, this investment plan is not able to generate enough activity in the economy to reduce unemployment. If we consider a long term fiscal sustainability, VAT financing of the investment plan is pretty harsh on the economy, as it affects all the agents in the economy. Moreover, this fiscal policy would not be 'pro-poor' because all households (including the poor) are hit by an increase in VAT. The alternative financing scenario would offer some political options to the government, as it will target only households that pay direct taxes or firms. An intermediate solution could this incorporate a combined burden sharing between households and firms. .

Figure 2 :

 2 Figure 2 : Impact on GDP

  Figure 4 : Impact on GDP (at basic prices)

Table 1 : The impact of an increase in the stock of public capital on output Author Focus of study Output elasticity of public capital Aschauer (1989) US National 0.39 Holz-Eakin (1988) US National 0.39 Munnell (1990a) US National 0.34 Costa, Ellson and Martin (1987) US States 0.20 Eisener (1991) US States 0.17 Mera (1973) Japanese regions 0.20 Munnell (1990b) US States 0.15 Duffy-Deno and Eberts (1989) US Metropolitan Areas 0.08 Eberts (1986, 1990) US Metropolitan Areas 0.03 Source: Table adopted from

 1

Table 2 : The impact of an increase in the stock of public capital on output in South Africa Author Infrastructure measure (on economic growth) Econometric technique Output elasticity

 2 Table adopted from Fourie (2006) and extended by authors.

	Abedian and van	Public authorities capital stock	OLS	0.33
	Seventer (1995)		Public sector capital stock	OLS	0.17
	Development Bank	Public authorities capital stock	OLS	0.25
	of South Africa		Cointegration	0.3
	(1998)		Public sector capital stock	OLS	0.15
				Cointegration	0.28
			Public sector infrastructure stock	OLS	0.17
				Cointegration	0.25
	Fedderke, Perkins	Electricity Generation	VECM	0.1-0.2 and rising to 0.5 after
	and Luiz (2005)				controlling for institutions
	Bogetic	and	Infrastructure measures on labour	VECM	0.2-0.4
	Fedderke (2005)	productivity
			Infrastructure measures on total	VECM	-0.6
			factor productivity
	Fourie (2006)		Electricity Generation	VECM	0.2
			Electricity Generation on a measure	VECM	0.38
			of equity performance
			Social Infrastructure	VAR	0.01-0.02
	Source:

Table 3 : Correspondence between occupations and skills level

 3

	Skill Category	Occupation
		Legislators (LEG)
	Skilled	Professionals(PRO)
		Technicians(TECH)
		Clerks(CLER)
		Service workers(SEWO)
	Semi skilled	Skilled agricultural workers(SKAG)
		Craft workers(CRAF)
		Plant and machine operators(PLAN)

Table 4 : Repartition of the labour force according to occupations and activities:

 4

	LEG	PRO TECH CLER SEWO SKAG CRAF PLAN ELEM DOM ONS TOTAL

Table 5 : South African investment plan:

 5

		Nov.-2010	Dec-2011	2012/13	2013/14	2014/15
	Economic services	161,9	197,3	217,8	228,2	230,1
	Energy	52,5	71,7	90,4	98,8	102,7
	Water and sanitation	14,4	17,8	20,6	19,9	19,8
	Transport and logistics	69,1	79,5	76,3	76,9	72,3
	Other economic services	25,8	28,4	30,4	32,5	35,2
	Social services	17,2	26,6	26,8	32,5	35,2
	Health	6,7	10	9,6	13,9	15,2
	Education	6	9,1	9,8	11,2	11,2
	Community facilities	3,5	5,2	4,7	4,8	6,2
	Other social services	1	2,4	2,6	2,6	2,7
	Justice and protection services	3,8	4,1	4,4	5,1	5,8
	Central government and	2,1	4,2	8	3,5	2,5
	Financial services	0,3	0,7	0,7	0,7	0,8
	Total	185,3	232,9	257,6	269,9	274,4
	Source: Medium Term Budget Policy Statement (2011), page 26 table 3.2

Table 6 : Impact on unemployment (% to Business as Usual (BAU))

 6

		LEG PRO TECH SEWO SKAG CRAF PLAN CLER	ELE M	DOM ONS
	2012	-13,93	-0,06 -5,06	-0,92	-1,72	-1,35 -1,27 -0,51 -1,88 -0,85 -0,74
	2020			-14,31 -25,64 -11,89 -15,46	-12,83	-15,08	-8,74 -7,07

Table 7 : Impact on Production (% to BAU)

 7

	Sectors

Table 8 : Impact on households' income (in % to BAU)

 8

		Labor income	Transfer income	Total income
	2012	0,20	-0,17	0,06
	2020	3,49	-3,26	0,91

Table 9 : Impact on firms' income and savings (in % to BAU)

 9

		YFK	YF	SF
	2012	-0.25	-0.22	-0.21
	2020	-4.64	-4.09	-4.00

Table 10 : Impact on government (in % to BAU)

 10

		Transfers income	Taxes on production	Taxes on products	Direct taxes on households	Direct taxes on firms	Total government income
	2012	-0,21	-0,24	0,12	0,06	-0,25	-0,01
	2020	-4,00	-1,26	0,86	0,91	-4,64	-0,63

Table 11 : Impact on unemployment for skilled workers (% to BAU)

 11

		LEG		PRO		TECH
		2012	2020	2012	2020	2012	2020
	FinA	42,75	50,39	67,29	171,88	37,11	73,07
	FinB	17,09		36,75	-32,04	18,05	-34,51
	FinC	156,38	877,84	185,02	1076,57	119,78	606,59

Table 12 : Impact on unemployment for semi-skilled workers (% to BAU)

 12

		CLER	SEWO	SKAG	CRAF	PLAN
		2012	2020	2012	2020	2012	2020 2012 2020 2012 2020
	FinA	4,46	13,99	1,49	-4,79	7,73	38,36 0,37 -8,44	2,82	6,33
	FinB	2,21	-0,81	0,40	-11,15	3,46	8,20 -0,40 -13,02 0,98 -6,14
	FinC	10,58	60,01	5,74	29,80	14,83 88,72 8,00 47,01 10,20 60,83

Table 13 : Impact on unemployment for low skilled workers (% to BAU)

 13

			ELEM	DOM		ONS
		2012	2020	2012	2020	2012	2020
	FinA	-2,77	-24,10	1,40	3,00	1,71	5,26
	FinB	-2,36	-21,81	0,39	-3,53	0,60	-1,61
	FinC	1,58	7,61	4,76	28,01	5,10	30,42

Table 14 : Impact on production (% to BAU)

 14

		FinA		FinB			FinC
		2012	2020	2012	2020	2012	2020
	Agriculture, forestry & fishing	-0,50	-1,20	-0,23	-0,43	-1,06	-6,00
	Coal mining	0,00	3,96	0,01	2,38	-0,82	-3,47
	Gold & uranium ore mining	0,55	10,05	0,47	8,46	-0,18	4,12
	Other mining	0,42	7,05	0,25	3,39	-0,19	0,97
	Food	-0,94	-4,52	-0,48	-2,20	-1,44	-8,53
	Textiles	-1,50	-6,84	-0,74	-2,97	-2,41	-14,41
	Footwear	-1,32	-6,27	-0,66	-2,89	-2,21	-13,29
	Coke & refined	-0,27	1,24	-0,09	1,12	-0,92	-4,60

Table 15 : Impact on households' income (% to BAU)

 15

		Labour income	Transfer income	Total income
		2012	2020	2012	2020	2012	2020
	FinA	-0,42	-0,34	-0,42	-3,31	-0,42	-1,47
	FinB	-0,14	1,45	-3,21	-23,21	-1,31	-7,96
	FinC	-1,59	-9,11	-1,01	-8,09	-1,37	-8,72

See Aghion and Howitt (2000).

For a detailed discussion of relevant papers in this field see[START_REF] Aghion | Endogenous Growth Theory[END_REF].

The term Dutch disease refers to a phenomenon where an economic boom (normally a discovery of natural resource or large inflow of foreign currency) leads to a real exchange rate appreciation that results in increased demand for non-tradables and eventually to a decline in the economic growth rate. It was coined to describe the deindustrialisation experienced in the Netherlands in the 1960s following the discovery of natural gas in the North Sea.

The list of equations is available upon request from the authors.

Note that in the CGE results, a real devaluation of the rand takes the form of a generalised reduction in domestic prices.

To specify the accumulation of capital, the Jung and Thorbecke (2003) function is followed.

This assumption can seem strange given that the country has in the past increased their savings from abroad. However, South Africa does not want to increase substantially its current level of borrowing.

Unskilled

Elementary occupations(ELEM) Domestic workers(DOM) Occupation unspecified(ONS)

The following table gives a repartition of the 11 different occupations by activity.


Abdoul-Razak Tidjani

Jean-Noël Lorenzi

Olivier Lespinet

Cyril Bontemps

Pierre Leblond

Massive gene flux in bacterial population fuels adaptation to forest soil ecosystem

Introduction

 Specific metabolites produced by variable genes could serve as public goods in inhibiting competitors but not other population members.

[1]Hao W., Golding G. B., (2006). The fate of laterally transferred genes: life in the fast lane to adaptation or death. Genome research, 16(5), 636-43.

[2]Wielgoss S., Didelot X., Chaudhuri R.R. et al. ( 2016

Strategy

The 11 sequenced genomes were compared with one reference chosen among the population (here RLB1_8). Each bar represents a strain chromosome. Triangles above a chromosome represent an insertion of genes in the reference strain and those below, an insertion in the compared strain. The size of the triangle is proportional to the number of genes in the indel.  This study revealed an unexpectedly genetic diversity in term of presence/absence of genes among a Streptomyces population.

 Massive gene fluxes can explain how such diversification can occur within short evolutionary time. The genomes are organized as linear chromosomes, some of them bearing large extrachromosomal elements Pairwise comparisons of genomes were performed to identify variable gene pools due to insertion or deletion events (indels)

The strains of a population are not necessarily clones and some can have specific genes that are not shared by all of the individuals of the population [1,2] . The real extent of this population genome variation in soil bacteria remains poorly described.

We aimed here to decipher by genome comparison the proportion of the specific genes, the mechanisms shaping these variations and their potential roles in a Streptomyces population.

Streptomyces are soil dwelling bacteria. They have a linear chromosome and a prolific specialized metabolism (e.g. enzymes, antibiotics, antifungals…) often of high biotechnological and medical interests. They can play key roles in the plant rhizosphere by contributing to their growth, health and resilience.

As for plant or animal, bacteria are naturally structured in populations, i.e. individuals of the same species that interact together in the same environment.

2 .

 2 To isolate a Streptomyces population we minimized the spatial scale by performing a micro-scale sampling (from grains of soil) Phylogenetic analyses were then performed to select strains belonging to the same species (e.g. identical strains in 16S rDNA )They are among the biggest bacterial genomes ever sequenced (ranging from 11.7 to 12.4 Mb)Phylogenomic analyses confirmed that the strains are closely related at the intra-specific level and derived from a recent common ancestor  Genome diversity impacts secondary metabolism among closely related strains.

  ); A barrier to homologous recombination between sympatric strains of the cooperative soil bacterium Myxococcus xanthus. The Isme Journal. 1-10. [3]Vetsigian K., Jajoo R., and Kishony R., (2011). Structure and Evolution of Streptomyces Interaction Networksin Soil and In Silico. PLoS Biol 9 (10): e1001184. doi:10.1371/journal.pbio.1001184.



  The diversity observed between individuals can have a functional role and impacts the ecology of the whole population. Strains in each coloured clade have identical 16S rDNA sequences. Strains in red in the purple clade were chosen to be sequenced 3. The genomes of 11 strains of the population were sequenced and compared.

indel events (triangles) ranging from 1 to 241 genes were identified in the population  Two strains can differ from 2 to 261 indel events revealing a great diversity in the population  There is a gradient of indel events toward the chromosomal ends

	Comparison of a diversity
	hotspot between 3 strains.
	The white boxes represent strain
	specific indels and grey boxes
	conserved regions. Brown and
	purple boxes represent gene
	clusters involved in secondary
	metabolite synthesis.
	The bioassay shows that only
	RLB1-9 can inhibit the growth of
	a Bacillus indicator strain.
	Mutation of the RLB1-9 NRPS
	showed that it was responsible
	of the Bacillus inhibition.
	Strains of the population do not
	inhibit each other in same
	conditions (data not shown).
	 Some hotspots of
	diversity have been
	identified (coloured triangles)
	 Many positions have
	mobile (conjugative)
	genetic element
	signatures, indicating that
	gene transfers can be the
	motor of the diversity

The highlighted triangles show examples of diversity hotspots where different type of indels are present in the population.

 452


Narjes Khatoon Fazeli
email: fazelinarjes@gmail.com

The effects of Metropolises noise pollution on human health and effective strategies to control and reduce it

Keywords: Noise pollution, Traffic noise, People's health, Hearing loss

Along with the advent of industry and the increasing population in cities and the spread of the use of motor vehicles, today noise pollution has been recognized as a threat to human health. Noise pollution is one of the major environmental problems of cities that, however, has been increasing in terms of intensity in recent years. Exposure to noise threatens human physical, mental and mental health. Noise pollution has a variety of sources, including motor vehicles in urban traffic and industrial plants operating in the city. Therefore, a number of laws are necessary to control and reduce noise pollution. The article deals with the sources of noise, standards related to noise levels, the effects of noise on human health and suggested ways to reduce and control the type of pollution.

Introduction

Population growth, coupled with industrial development and metropolitan technology, creates many problems for people, with environmental pollution being one of the most important problems. Noise pollution -as one of the most important environmental pollutants -contributes significantly to such problems [START_REF] Riahinia | The Impact of Electric Transportation on Noise Pollution in Tehran Metropolis[END_REF][START_REF] Kim | Summary of night noise guidelines for Europe[END_REF]. Noise, however, is an integral part of modern and quasi-modern life. In recent years, science and technology together have made remarkable progress in all areas of industry, so that one of the major dangers of industrialization is noise pollution [START_REF] Roshani | Evaluation of Hearing Loss in Megamotor Factory Workers in Summer of 2008, 4th Iranian Occupational Health Conference[END_REF][START_REF] Mobasseri | Traffic noise and it's measurement methods[END_REF][START_REF] Tabaraei | Study of the Effective Parameters on the Making Use of Protective Devices in Qom Province Workers Community in[END_REF]. In acoustic science, noise is defined as audible acoustic energy, which has a positive and negative effect on the physical and mental health of living creatures [START_REF] Madadi | Modeling the Propagation of Noise Pollution from Isfahan's West Ringway in Ghamishloo Wildlife Refuge Using SPreAD-GIS[END_REF][START_REF] Tervo | High source levels and small active space of high-pitched song in bowhead whales (balaena mysticetus)[END_REF]. Sound is caused by the vibration of air molecules and constant pressure changes. Therefore, acoustic waves are types of mechanical waves that propagate in the form of longitudinal waves and allow the ear to hear [START_REF] Riahinia | The Impact of Electric Transportation on Noise Pollution in Tehran Metropolis[END_REF]. In most developed and developing countries, particular attention has been paid to the issue of noise pollution, as effective measures have been taken to formulate and enforce the laws to reduce or eliminate noise pollution [START_REF] Riahinia | The Impact of Electric Transportation on Noise Pollution in Tehran Metropolis[END_REF][START_REF] Kim | Summary of night noise guidelines for Europe[END_REF][START_REF] Roshani | Evaluation of Hearing Loss in Megamotor Factory Workers in Summer of 2008, 4th Iranian Occupational Health Conference[END_REF][START_REF] Mobasseri | Traffic noise and it's measurement methods[END_REF][START_REF] Tabaraei | Study of the Effective Parameters on the Making Use of Protective Devices in Qom Province Workers Community in[END_REF][START_REF] Madadi | Modeling the Propagation of Noise Pollution from Isfahan's West Ringway in Ghamishloo Wildlife Refuge Using SPreAD-GIS[END_REF][START_REF] Tervo | High source levels and small active space of high-pitched song in bowhead whales (balaena mysticetus)[END_REF][START_REF] Kiani | Noise pollution assessment in Khoramabad to presenting executive strategies to control or reduce it[END_REF].

Sources of noise pollution

Nowadays, with noise pollution being considered as one of the environmental pollution and its negative effects on human life, experts have investigated some of the factors causing noise pollution, such as car and noise pollution due to urban traffic which is the main cause of noise and air pollution [START_REF] Riahinia | The Impact of Electric Transportation on Noise Pollution in Tehran Metropolis[END_REF][START_REF] Marathe | Traffic noise pollution[END_REF][START_REF] Manuel | Highway Traffic Noise Analyzed.an abatement Policy and Guidance[END_REF].

The noise levels caused by road vehicles, however, have a major share in the noise generation of cities. Examples of noise pollution that threaten the health and well-being of living creatures, especially humans, are: Population growth, motor vehicle overload, road traffic, aircraft noise, presence of some industrial plants within city, urban and interurban trains and etc. [START_REF] Riahinia | The Impact of Electric Transportation on Noise Pollution in Tehran Metropolis[END_REF][START_REF] Kabad | Geographical Information System for Traffic Noise Analysis & Forecasting with the appearance of barriers[END_REF]. During the earthquake and its short period of time, the closer the source of the earthquake, the closer the source of the noise is due to the release of energy from the rapid failure of the crustal faults. Lightning is a severe and very rapid electrical discharge in the air that produces electricity and high noise; the sound is caused by the split layers of air.

Abnormal source of sound 2.1.2.1. Fixed sources of sound

Because of the equipment like gas turbine, compressor, combustion chamber, strong fans, etc., gas power plant produce a lot of noise which is annoying to the people around them.

Non-fixed sources of sound

Until a few years ago, car makers and designers focused on reducing car fuel consumption; while today the focus has been shifted towards reducing the noise emitted by cars. Using either public vehicles (city trains) or personal vehicles (cars and motorcycles) produce noise pollution. However, the noise caused by cars is due to a variety of reasons, such as the use of horns, automotive aerodynamics, car engine design, tires, car mirrors; and the noise of urban trains is due to intersections in the direction of movement, electric motors, rotation on rails and stops [START_REF] Mobasseri | Traffic noise and it's measurement methods[END_REF].

Internal sources of Sound

All available electrical equipment, due to its electric motor and its performance, generates sound. In internal combustion engines or in combustion engines, the fuel mixture and the oxidizer are reacted inside the enclosure chamber; this process being accompanied by the generation of noise.

Acoustic Pollution Standards

In the last two decades, in most countries, special attention has been paid to the issue of noise pollution, which in turn has led to issuing some regulations and laws to deal with noise pollution [START_REF] Riahinia | The Impact of Electric Transportation on Noise Pollution in Tehran Metropolis[END_REF][START_REF] Malanoutian | m Noise population[END_REF]. In Iran, measures have been initiated to deal with environmental pollutants. In this regard, two types of standards have been established for determine sound effects in Iran:

1) Emission Standards: Applying the standards, one can calculate the output levels of sound sources [START_REF] Riahinia | The Impact of Electric Transportation on Noise Pollution in Tehran Metropolis[END_REF]13]. 2) Environmental standards: The standard sets the threshold limit for different urban environments [START_REF] Riahinia | The Impact of Electric Transportation on Noise Pollution in Tehran Metropolis[END_REF]13].

Table 3 shows the environmental standards of Iran, which are announced by Iranian Department of Environment. According to the table, urban areas of Iran are divided into five categories [START_REF] Riahinia | The Impact of Electric Transportation on Noise Pollution in Tehran Metropolis[END_REF].

The effects of noise pollution

Noise, just like all pollutants, reduces the quality of life and creates significant health risks. The steady increase in the volume of highway traffic in urban areas has increased the number of problems affecting life. The high number of cars, their speed as well as the use of nonstandard horns causes problems in the areas around the highways [START_REF] Mousavi | The First International Conference on Iranian Natural Hazards and Environmental Crises, Solutions and Challenges[END_REF][START_REF] Sadeghi | Assessment of Changes in sound levels ten-year in Shahrekord city[END_REF]. Researches indicate that destructive noise can have an adverse effect on different organs of the human body and organ [START_REF] Mobasseri | Traffic noise and it's measurement methods[END_REF][START_REF] Tabaraei | Study of the Effective Parameters on the Making Use of Protective Devices in Qom Province Workers Community in[END_REF][START_REF] Mousavi | The First International Conference on Iranian Natural Hazards and Environmental Crises, Solutions and Challenges[END_REF][START_REF] Al-Mutairi | Measuremants and Model Calibration of Urban Traffic Noise Pollution[END_REF][START_REF] Stunsfeld | Noise, noise sensitivity and psychiadric disordes: epidemiology and psychophysiological studies[END_REF]. Approximately, 120 million people in the European Union are exposed to traffic noise of about 55 db. and over 50000 people are exposed to traffic noise of about 45 db.

The most common side effects of noise are human hearing loss. Even non-annoying but persistent sound is harmful to humans and reduces the sensitivity of the ear to the variety of sounds. Hearing a 100 dB sound for 10 minutes requires a 20-minute rest in a quiet environment to compensate for the effect. Also, working in noisy environments destroys hearing cells and results in "irreversible hearing" which is so called as "job deafness". Continued exposure to noise, along with other factors such as aging, background of some diseases, can accelerate hearing loss [START_REF] Golmohammadi | Sound and vibration engineering[END_REF][START_REF] Aghilinezhad | Occupational Medicine and Occupational Diseases[END_REF].

Although noise-induced hearing loss is preventable, however, it is currently one of the major general health problems and consider as one of the major diseases caused by prolonged exposure to noise and irreversible [START_REF] Halvani | Noise induced hearing loss among textile workers of Taban factories in Yazd[END_REF][START_REF] Katz | Hand book of clinical audiology[END_REF][START_REF] Mcbride | Noise induced hearing conversation in mining[END_REF][START_REF] Williams | Hearing loss and protection of noise in the workplace among rural Australians[END_REF][START_REF] Perez | Effect an initial noise induced hearing loss subsequent noise induced hearing loss[END_REF][START_REF] Sulkowski | Epidemiology of occupational noise induced hearing loss (ONIHL) in Poland of a laryngol pol[END_REF][START_REF] Wójcik | Assessment of exposure to noise at various workplaces in a chemical plant[END_REF][START_REF] Minja | Noise induced hearing loss among industrial workers in Dares salaam[END_REF][START_REF] Aghili Nejad | Occupational medicine[END_REF].

Approximately, 1 to%4 of the population of developed countries is exposed to dangerous levels of noise, which means that the hearing protection program is somewhat ineffective in these countries [START_REF] Ghazaie | Diseases and complications of physical factors[END_REF].

Examples of the reverse effects include: nervous tenderness, severe irritability, muscle cramps, dizziness, physical and emotional exhaustion, sleep disturbances, impaired vision, increased heart rate and blood pressure, convulsions, aggression, and ultimately general body weakness [START_REF] Mobasseri | Traffic noise and it's measurement methods[END_REF][START_REF] Tabaraei | Study of the Effective Parameters on the Making Use of Protective Devices in Qom Province Workers Community in[END_REF][START_REF] Halvani | Noise induced hearing loss among textile workers of Taban factories in Yazd[END_REF][START_REF] Panahi | Fundamental of Environmental Health[END_REF][START_REF] Choobine | Fundamental of Occupational Health[END_REF][START_REF] Berger | Development of a new standard laboratory protocol for estimating the field attention of hearing protection devices, Part 111, the validity of using subject-fit date[END_REF][START_REF] Ward | Endogenous factors related to susceptibility to damage from noise[END_REF][START_REF] Hetu | Impact of occupational hearing loss on the lives of workers[END_REF]. Sound pollution even affects insulin secretion [START_REF] Mousavi | The First International Conference on Iranian Natural Hazards and Environmental Crises, Solutions and Challenges[END_REF][START_REF] Armario | Chronic noise stress and insulin secretion in male rats[END_REF]. In the study of social injuries, complications such as fatigue, aggression, irritability, and family problems are common [START_REF] Packard | Industrial production health and disease in sub-Saharan Africa[END_REF][START_REF] Saremi | Non-auditory Effects Caused by Environmental Noise Pollution[END_REF]. Many environmental experts believe that in the past few years, noise pollution has come under the influence of other pollutants.

Noise pollution control

To prevent the psychological and social effects of noise, the World Health Organization (WHO) has stated that the noise level should not exceed 55 dB at days and not 40 dB at nights [START_REF] Mousavi | The First International Conference on Iranian Natural Hazards and Environmental Crises, Solutions and Challenges[END_REF][START_REF] Berglund | Guidelines for community noise[END_REF][START_REF] Wrof | Night noise guidelines for Europe[END_REF]. Several methods have been proposed to reduce the emission of noise pollution caused by car traffic, as following:

1) Creating Distance: creating distance between highways and the surrounding residential areas; in fact, residential areas can be built at a distance from the highways [START_REF] Mobasseri | Traffic noise and it's measurement methods[END_REF].

2) Use of semi-soundproof walls: These walls, known as acoustic barriers, are located between highways and residential houses [START_REF] Mousavi | The First International Conference on Iranian Natural Hazards and Environmental Crises, Solutions and Challenges[END_REF][START_REF] Golmohammadi | Vibration and sound engineering, hamadan[END_REF]. Proper and practical barriers can reduce traffic noise by 10 to 15 db. The obstacles can be hill of suitable height, barrier walls made of wood, stone, plaster, concrete or other suitable materials. 3) Suitable vegetation: Building and expanding green space is one of the best ways to reduce traffic noise. In addition to reducing the level of noise pollution, green space has good psychological and psychological effects on people [START_REF] Kiani | Noise pollution assessment in Khoramabad to presenting executive strategies to control or reduce it[END_REF]. If these types of coatings are suitable in terms of length, width, thickness and density, they can reduce noise by up to 10 dB, while this type of insulation is economically and sustainably recommendable. 4) Insulation: Using double glazed windows in houses is one of the most effective solutions. 5) Prohibition of worn-out vehicles: A ban on the use of worn-out cars can have a positive effect on noise reduction. 6) Prohibition and penalties for vehicles using non-standard horns: another possible way is to ban on vehicles which using non-standard horns and make traffic noise.

7) Identifying and fixing the noise factors in the car:

-Engine: The most important source of noise in the car is noise due to combustion, injector and cooling fan. -Vehicle Components: Changes to materials used in the manufacture of automotive body parts, or the use of high-density steel or plastic sheets, and the placement of steel fasteners to reduce and dampen the vibration energy of valve caps. -Gear Noise: If each gear is fully fixed and adjusted, with good lubrication and improved gearshell, it has a good effect on noise reduction.

8)

Training and promotion of traffic culture [START_REF] Mousavi | The First International Conference on Iranian Natural Hazards and Environmental Crises, Solutions and Challenges[END_REF]. 9) Electric vehicles are considered as one of the best options for replacing ordinary vehicles (internal combustion engine). One of its reasons is its environmentally friendly properties. One of the most important features of electric vehicles is its environmentally-friendly features which is the replacement of the electric motor rather than the internal combustion engine, so it can be a good mean to reduce noise pollution in cities [START_REF] Campello-Vicente | The effect of electric vehicles on urban noise maps[END_REF].

It should be noted that due to the altitude, air temperature and humidity available, the acoustic energy emitted by the vehicle's motion is absorbed by the atmosphere (table 2) [START_REF] Madadi | Modeling the Propagation of Noise Pollution from Isfahan's West Ringway in Ghamishloo Wildlife Refuge Using SPreAD-GIS[END_REF][START_REF] Gharibi | Analysis of most important environment and traffic effects on emission of Lmax in Tehran-Mashhad highway[END_REF][START_REF]Method for Calculation of the Absorption of Sound by the Atmosphere[END_REF][START_REF] Harris | Absorption of sound in air versus humidity and temperature[END_REF]. Smooth and hard surfaces -such as road surfaces-absorb little noise, while soft, porous surfaces -such as meadows and grass-weaken sound levels. Also, proper vegetation can absorb sound/noise waves [START_REF] Madadi | Modeling the Propagation of Noise Pollution from Isfahan's West Ringway in Ghamishloo Wildlife Refuge Using SPreAD-GIS[END_REF][START_REF] Pijanowski | Soundscape ecology: the science of sound in the landscape[END_REF]. The intensity and frequency of acoustic perturbations depend on the characteristics of the sound or noise source. For example, the noise caused by moving vehicles on roads depends on the number, type, and speed of movement, vehicle body design, wind speed and direction [START_REF] Madadi | Modeling the Propagation of Noise Pollution from Isfahan's West Ringway in Ghamishloo Wildlife Refuge Using SPreAD-GIS[END_REF].

Accordingly, noise maps have been developed in many large and small cities around the world and extensive studies have been conducted to reduce noise [START_REF] Mobasseri | Traffic noise and it's measurement methods[END_REF][START_REF] Mobasseri | Impact of driving style on fuel consumption[END_REF][START_REF] Mobasseri | A Comparative Study Between ABS and Disc Brake System Using Finite Element Method[END_REF]]. Take, for example, London's Noise map (Europe's largest metropolitan area) or some parts of Madrid (Europe's third largest metropolitan area).

Noise measurement is a prelude to identifying priorities in identifying areas and areas of influence and judging the outcome and practices of traffic control; in fact, these researches have been conducted to determine the exact location of hospitals, medical centers or similar centers. In this context, several international valid standards have been defined in which the methods of measuring noise and permissible noise levels are discussed [START_REF]ISO 362[END_REF][START_REF]Acoustics Measurmant of noise emitted by stationary road Vehicles-Survey method[END_REF].

Traffic noise level depends on several factors that can be divided into three categories:

Conclusion

Noise pollution has grown dramatically in recent years due to both natural and social phenomena. Population growth coupled with industrial development and metropolitan technology poses many problems for urban dwellers, with noise pollution being one of the most important problems.

There is a positive relationship between industrial technology and noise pollution; in other words, as technology evolves, the problem of sound also becomes wider and causes more problems. The problem of this type of pollution, however, is considered as one of the most important environmental issues in most industrial areas and has even been considered in macro management of urban planning, interior architecture, residential and commercial. Researches show that the distractions of today's life create physical, mental, and psychological distress for metropolitan citizens. Therefore, it is necessary to regulate the control of this type of pollution and to establish appropriate laws to prevent, reduce and control it.

Present paper deals with the sources of noise, standards related to noise levels, noise effects and suggested strategies to reduce and control this type of pollution have been granted. Using electric vehicles as well as expansion of vegetation in noisy environments are among two examples of noise pollution control strategies.

2. 1 . 2 . 1 . 1 .

 1211 Sound sources are divided into natural and abnormal categories: Normal sources of sound

Table 1 :

 1 Standard Pressure Limit Level of Air Pressure in Iranian Open Air[START_REF] Riahinia | The Impact of Electric Transportation on Noise Pollution in Tehran Metropolis[END_REF][START_REF] Mobasseri | Traffic noise and it's measurement methods[END_REF]13].

	Type of area	7 o'clock to 22 o'clock 22 o'clock to 7 o'clock
	Residential	55	45
	Residential-Commercial	60	50
	Commercial	65	55
	Industrial -Residential	70	60
	Industrial	75	65

Table 2 :

 2 Meteorological data used in sound/noise simulation[START_REF] Madadi | Modeling the Propagation of Noise Pollution from Isfahan's West Ringway in Ghamishloo Wildlife Refuge Using SPreAD-GIS[END_REF].

	Season temperature	Relative humidity percentage	Prevailing wind direction	prevailing wind speed (Miles per hour)
	Winter	43.6	56.5	135	5.7

1 )

 1 Traffic volume (Traffic noise of 2000 cars per hour is twice that of 200 cars per hour) (table 1); 2) Traffic speed (Traffic noise at 65 mph is about twice that of 28 mph at 55 mph). 3) Type of vehicles in traffic (The noise from a truck at 55 mph equals the noise of 28 riders at 55 mph)

Table 3

 3

presents the daily and annual traffic volumes of vehicle types in a given route.

Table 3 :

 3 Annual and daily traffic volumes of vehicles[START_REF] Madadi | Modeling the Propagation of Noise Pollution from Isfahan's West Ringway in Ghamishloo Wildlife Refuge Using SPreAD-GIS[END_REF]

	The parameters	sedan	Van	Bus	truck
	Annual number of each type	96212 14994 25590 36111
	Daily Percentage	38.5	6	10.2	14.5
	Two-thirds of traffic a day	25.8	4	6.9	9.7
	Ten hours daily activity	64.5	10	17.1	24.2
	The number of cars per minute at an average speed of 80 km / h 21.5	3.3	5.7	8.1
	Distance of cars on each track	1256.6 8062.9 4724.4 3347.9
	Number of cars per route	11	2	3	4
	Average sound intensity	73.7	77.9	76	78.1