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Economy of the White Sea watershed area 7 page

Karelia is the least industrially developed region. As it was pointed out above, it mostly relies on the forestry sector, and thus on the national economic policy. Volumes of metallurgical production will decline unless new deposits are developed. Other industrial branches will not contribute significantly to the repub- lic s economic growth. The geographic position (vicinity to the state border) will promote the development of transport, communication, tourism, and possibly several other sectors (Revival .. ., 2003).

 

Gas and oil

From the strategic standpoint, the Russian reserves of natural gas and oil allow prediction of their intense development during the forthcoming decades. The Stockman gas field discovered on the Russian Arctic shelf in 1988 comes to the foreground. According to some estimates, its development up to a commercial level of gas production may cost up to US$ 25 billion. The first drilling platform is expected to start operating in 2006. Its rated capacity is 30 billion m3of gas per year. The second platform is planned to start in 2010, and the third in 2015. In total, it is anticipated they will pump out about 90 billion m3of gas annually. The total stock of the deposit is estimated at 3.2 trillion m3, making it one of the biggest in the world. The gas pipeline is to be laid across the ocean floor, and it will emerge from the ocean near the town of Teriberka to further run across the Kola Peninsula, covering a distance of approximately 470 km through rivers, bogs, and rocks up to the city of Volkhov. From Volkhov, the piped gas will be delivered abroad or to Russian gas-processing plants. The implementation of this highly ambitious and expensive plan, as well as the maintenance of this complex, will employ about 12,000-15,000 workers. Besides this, the construction of bridges, spur tracks for maintenance, and residential areas for workers must be carried out. Some boiler- houses may be converted to gas-heating plants. This will cut down on the expenses and improve the ecological situation. The budget of the oblast, too, will gain income from the Gazprom company for use of its territory.

Additionally, the Timan-Pechora gas-oil field should be mentioned. Since the beginning of exploitation of the Timan-Pechora deposits and oil transport from this area, some new projects have been developed. The administration of the oblast and the management team of the Lukoil company are considering the possibility of


 

building a refinery in the Kola Peninsula. The preliminary cost of the project is US$ 200 million. The complex is designed to refine 3 million tons of oil annually. An oil distillation station with a rated capacity of 7 million tons is also planned to be built.

 

Diamonds

Regarding diamond reserves and quarrying output, Russia holds the leading position in the world, and the White Sea Basin will play an important role in the future development of the diamond industry.

 

Timber resources

The White Sea Basin has traditionally been characterized by a well-developed timber industry. As timber resources of the area are still extremely highly valued, further development of the timber industry is predictable.



 

 

7.7.3 Specific features of regional forecasting based on the scenario approach

By forecasting regional development one can assess the existing potential problems, identify negative tendencies, determine the effects of the global market and national authorities, and suggest the most efficient solutions to both the regional authorities and business community.

It needs to be mentioned that forecasting has become more challenging. Specific features of the social, political, and economic circumstances determine the specificity of forecasting during a period of transition. The transition to a new social order is accompanied by fundamental changes in the state structure, strongly variable relations with the New Independent States (NIS) and Baltic States, disintegration of political and economic unions, and the loss of certain markets by Russian companies. The Russian Federation has periodically been suffering political crises. Not infrequently, the Russian authorities act chaotically, only rarely following some definite policy (Hodacheck et al., 2000; Uzyakov, 2002).

The Soviet Union had a so-called planned economy, with multi-year plans adopted in advance. Regional economies were strictly linked to the state economy. This allowed assured estimates of regional development. This is not the case any more. The plans of socio-economic development reported in the press are prepared by different associations rather than by the state agencies, and their number is rather modest. Besides, the number of state enterprises in industry is constantly decreasing. As a result, there is an absence of reliable information on the actual state and further development of private enterprises/industry for the public, impeding any kind of economic and/or demographic analysis.

The development of regional economies is strongly influenced by the political instability in Russia, the possibility of significant changes in economic policy, and frequent changes made by the authorities of the region. In most regions, there is no mutual understanding between the authorities, the business community, and people; support to business from the authorities is often based on favoritism rather than its


 

efficiency and innovative or investment activities. Accordingly, private businesses conceal their income from taxation, reporting low profits and wages. Integrated business groups and sectoral and regional associations of entrepreneurs are forming very slowly. Legislation remains faulty, especially concerning bankruptcy, which poses a threat to investment into efficient businesses.

The contemporary economy in Russian regions develops in an unsteady manner. Economic indices change rapidly, and significant structural shifts occur. This seriously encumbers any solid analyses for future long-term periods. Frequent changes of owners and managers add to the ambiguity.

In a transitional economy, where the uncertainty factor for the major indices of regional development is high, the scenario approach to forecasting becomes particu- larly applicable. In this approach, various situations can be predicted and the outcomes of different actions of regional authorities can be assessed. A crucial step therefore is to determine the approaches to generating scenarios. The basis for working out regional development scenarios (options of the policies to be pursued by the regional and national authorities and their effects on the distribution of resources) is the analysis of retrospective and current situations in the region s economy. The principal trends in the regional development, structural changes that have occurred in the production sphere, and the effect of foreign economic factors are to be analyzed.

The parameters of regional socio-economic development, employed in different stages of forecasting, must be comparable, and the information supporting the analysis and forecasts must correspond to the actual situation in the regional economy. The high inflation rate in the early 1990s posed a serious problem for developing comparable data series. For most parameters, the problem can be cir- cumvented via the State Statistical Department data as well as dedicated models suggested by ad hoc expert groups.

The methods used to obtain statistical indices often change, also creating problems in obtaining comparable information. Transition to new forms of book keeping and statistical reporting, and their frequent changes, also resulted in significant errors, both due to the faults inherent in the forms themselves and due to mistakes made when filling them in. Enterprises did not strive for accuracy either, sometimes even distorting figures on purpose, especially in small and medium-scale businesses. Entrepreneurs are interested in under-estimating the actual outcomes of their activities, as it may reduce their due taxes. Therefore, the proportion of the shadow economy is quite significant (Druzhinin, 2000; Nastenko and Vasina, 2002).

For economic processes to be forecast successfully, specific approaches and models, for which long-term data series are not required for calculations and which would be convenient for comparing several options, need to be developed. To make the forecast more accurate, conceptual analysis of the region s economy, its structure, and relationships between individual sectors and branches is necessary. Regional forecasts must reflect the aims, tasks, and opportunities of regional devel- opment. They have to rely on regional interests and take national interests into account, or at least agree with them (Diebold, 1998; Fildes and Stecler, 2002).


 

The following major tasks are to be solved in the course of regional forecasting (Kotilko and Sanin, 2001; Methodology .. ., 2003):

 

• Analysis of the status and development trends of the regional economy and external environment, identification of existing and potential problems and disproportions in the region and the main ways in which they can be eliminated.

• Selection of forecast methods and the development of predictive models and technology where external impacts and regional management potential are taken into account.

• Determining the alternatives of external impact and management of the region s economy, and identification of the corresponding scenario conditions (predictive model parameters).

• Determination, using the methods and models selected, of the possible ways (trajectories, directions, and scenarios) of the region s development and the factors (national and regional economic policy, global market, and other external impacts) leading to their realization.

• Assessment of the social, environmental, and political consequences of the region s economic development by the scenarios.

• Determination of the criteria for the selection of the most beneficial alternatives for the region s development and identification of the regional development scenarios best suiting these criteria; shaping the main lines of the regional economic policy in conformity with the best scenarios.

• Estimation of the resources and time necessary to implement the selected scenarios of regional economic development.

• Identification of the limitations and risks related to the potential changes in national policy and the domestic and/or global economy.

 

The following principles of regional forecasting can be listed:

 

• An integrated approach (i.e., consideration of all aspects of life in the region, viz. social, environmental, scientific, and technical, as well as the availability of data on natural resources for economic forecasting).

• Systemic (hierarchical) approach, in which the regional economy is necessarily considered as part of the national economy (scenario conditions are referenced to the national economic policy).

• Specificity or account of regional distinctions, since the geographical position, climate, availability of natural resources, structure of the economy, and other characteristics determine the dynamics of the principal developmental param- eters in the region.

• Coordinated approach, including the account of the interests of authorities, business, and society - a combination of the sectoral and territorial approaches.

• Availability of variants (alternatives) - when developing scenarios that must cover nearly all potential variants of change in the national and regional economic policy. Variations in financial, labour, and other resources.

• Responsiveness to feedback - combination of the explorative and normative


 

approaches to forecasting, development of new scenario conditions and subse- quent recalculations with regard to the results of forecast analysis based on the scenario conditions suggested at the previous stage.

• Adequacy (reliability) of initial data, realism of scenario conditions, and their forecasts.

• Cost efficiency (optimality), meaning that the benefits from using the forecast must be greater than its costs. Required accuracy and reliability should be ensured while keeping the forecast development costs as low as possible.

 

The process of regional forecasting based on the scenario approach falls into four stages:

 

• Identification of the region s specific characteristics, assessment of the ten- dencies, and identification of sectors with differing characteristics.

• The model development and its substantiation with due regard to the specific features of the region and identified sectors.

• The development of scenario conditions (options of the economic policy and the effects of the global market).

• Estimates of required resources, time, and reliability.

 

It is important to avoid gaps or boundaries between the stages, each commenc- ing during the implementation of the previous one. The model should be simple enough to obtain the results quickly and interpret them easily (e.g., on the basis of production functions as rates):

bY = EK bK + EAbA + p (7.7)

where bY = dY /Y , bK = dK/K, and bA = dA/A are logarithmic derivatives; p is the neutral technical progress rate; Y (t) is the GRP, where t is the year; K(t) is the cumulative investment over several years; A(t) is the number of employees; and EK, EA are the elasticity factors determined in the course of forming scenario conditions. Scenario conditions differ depending on the regional policy and external conditions (effects of the national policy and global market). Scenarios proper appear in the fourth stage, when calculations are made using the set of the scenario conditions, and when quantitative and qualitative estimates of regional development are obtained (Druzhinin, 2000).

 

7.7.4 Scenarios of probable White Sea Basin industrial development

To minimize the number of uncertainties present, it is suggested that the rate of GRP change is used as the main parameter of any scenario. The experts have formulated several probable scenarios of the White Sea Basin s industrial development. They are, to some extent, based on the analysis of the political and economical situation in Russia and in the world.

In the approach suggested, the forecast of regional economic development is based on the definition of the response of business to the actions of authorities and


 

changes in external settings by means of ad hoc models, which take the structure of the regional businesses into account. The dynamics of regional production is predicted using the equations of production function (PF). The common model is adapted to a specific type of structure. Equations of decomposition of the elasticity factor are used to bring different forecasts to agreement, and to assure that produc- tion structure will best meet a number of criteria designed for the region.

 

Pessimistic scenario with extrapolation

Scenario conditions encompass the fixed national economic policy. Development options depend on the dynamics of world prices of raw materials (oil, gas, and timber). Regional authorities have few possibilities to influence various current processes, although some administrative influence on the institutions controlling raw materials is feasible, and a minor part of the investment will be redirected to manufacturing. The effect of the national currency devaluation is over. Raw resource branches of the industry have reached the peak of their development because of the market limitations and rising costs. Most investments into industry with outdated equipment/technology will be spent on sewage disposal and modernization and/or substitution of obsolete equipment. This will lead to no increase or even a decrease in the income of these branches. As a result, the structure of the industry will not change. The annual GRP increase will be not more than 2%. In the long run, the development of new oil and gas deposits will raise the economic growth rates up to 3-5%, first owing to an increase in extraction and then to other related sectors. The growth rates after this will fall to zero again. If world prices of raw materials drop, a new devaluation, although of a smaller scale, may ensue, triggering a new surge of economic growth, which will however stay within 5-10% in the first year after the devaluation and rapidly return to the same 0-2% per year afterwards. A similar problem will arise if raw material prices increase sharply, because the influx of oil dollars will then cause a decline in the ruble. More problems will emerge if mines and quarries cease operating because of a decline in raw material prices. An outflow of the residents from northern areas will then intensify and the infrastructure will degrade rapidly. Re-staffing with qualified personnel and reconstruction of what has been destroyed will then be only possible in the case of very large deposits and will require much greater costs. The GRP will be decreasing, although only by 1-3% a year.

 

Reorganization of natural monopolies

Scenario conditions include a more active national policy concerning raw material monopolies, restructuring of state monopolies, and changes in the taxation of nature exploiting enterprises. Options are determined by the reorganization scheme adopted (the interests of extracting and processing businesses differ strongly, so different restructuring terms can be negotiated when joining the WTO), by fluctuations (not really significant) of raw material prices in the world market, and finally by the essence of changes in the taxation system. As in the previous scenario, the influence of regional authorities is minor. A relatively bad course for events would


 

be the formation of private natural monopolies capable of controlling regional auth- orities and only slightly dependent on federal authorities. In this case, the tariffs for businesses and the public may grow sharply, and the GRP will fall fast by 10-15% and keep decreasing at a somewhat slower rate for 1-2 years. Then again, some growth will follow, possibly with periodic devaluations caused by the significant changes of world prices of raw materials and with some fluctuations related to the implementation of the projects aimed at developing new large deposits. A somewhat better result can be achieved if a system of controlling the tariffs of natural mono- polies is created and the taxation system is modified (the tax pressure on extracting industries is raised with a simultaneous alleviation of the pressure on manufacturing sectors through increasing rent payments). Even if the tariffs grow sharply, the decline will depend on resource consumption of enterprises, so that the companies implementing resource-saving projects and introducing modern technologies will be in a better position. As a result, a 10-20% decline in the GRP over several years will be followed by appreciable structural shifts. Resource processing enterprises and related sectors will start developing at a higher rate. The growth rate in this case will increase to 2-4%.

 

 

Active industrial policy (optimistic)

The Russian authorities can become more active by stimulating development of promising sectors and reforming the established relationships between budgets, thus creating the conditions for a more active regional economic policy. Options may differ depending on the policy of the regional authorities (who may neglect arising opportunities), and, initially, on the dynamics of world prices for raw materials and the WTO membership terms (for a part of the services sphere). Technological reconstruction of existing industrial enterprises that use natural resources may lead to a 2-4% increase in the annual GRP by 2005 with a further increase afterwards. The vicinity of both St. Petersburg and a wealth of industrially developed countries, as well as the availability of great scientific potential, facilitate the solution to the problem. The federal and regional authorities can create such conditions that will stimulate innovations and promote the development of an innovative infrastructure in the region. This may accelerate both the pursued struc- tural reforms and the development of new sectors with a result of a 2% increase in the GRP by 2005, which may go on to reach a value of 5-7%.

Application of socio-economic indices with corresponding weights describing environmental impacts promises to give a quantitative input for further use in simulations. Most scenarios give an estimated growth/decline of the GRP. The behavior of the II vs. the GRPI is to be analyzed using available past data. The II values corresponding to the planned GRPI are to be determined. The next step is to analyze the combination of the II indices with the corresponding environmental index to eventually estimate the desired value with regard to the planned GRPI. This will give an integral estimate of the environmental impact expressed in terms compatible with the statistical model.


 


Date: 2016-03-03; view: 666


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