روابط متقابل بین سیاست های اقتصادی و شاخص های کشاورزی محیطی: یک چارچوب تحقیقی با نمونه هایی از آفریقای جنوبی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|24399||2000||15 صفحه PDF||سفارش دهید||7964 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Ecological Economics, Volume 34, Issue 3, September 2000, Pages 363–377
A number of methodological approaches to understanding and quantifying the potential impacts of changes in macroeconomic and sectoral policies on the natural resource environment have been developed in recent years. However each has its limitations, resulting in policy change still being implemented without due attention to environmental impacts. Two key drawbacks of those methodologies that do attempt to model these impacts are that they are generally static in their approach, thus may not alert the decision maker to the often quite different long-term implications, and that they attempt to generate rather specific sets of indicators, making them difficult to use and/or interpret outside case study applications. In this paper we expound a framework for addressing these limitations in the context of the agriculture sector. In developing countries in particular the dynamic dimension is critical given the twin pressures of population growth and rising incomes associated with economic growth. In light of the second drawback, it is the propensity of policy to impact upon the natural resource environment via its effect on the type of farming practice adopted that forms the focus of the paper. A methodology is first developed to facilitate the tracing of likely impacts of both price and non-price reforms, via both the incentives and constraints to increased food production. By separating out the impacts on environmental indicators associated with extensification and intensification of agriculture, it is possible to determine which of these indicators are most likely to be affected by policy changes, and to what degree in both the short and longer term. The framework is then applied to case study data from the South African agriculture sector to demonstrate how consideration of the risk of natural resource degradation earlier in the policy dialogue process could result in the implementation of more effective complementary measures.
Two central themes of the so-called Global Food Debate, (see, for example, Brown, 1994, McCalla, 1994 and Islam, 1995) surrounding the issue of increased agricultural production in poor countries are the influence of the technologies currently deployed and likely to be developed in the future, and the impacts on the natural resource environment as it comes under increasing pressure from attempts to raise the productivity of the resources used in agricultural production. The relationship between the technological choices and the policy environment are well documented, but the link between policy change and the natural resource environment has received much less attention. While the impacts of specific localised changes in the socio-economic context have received substantial focus, for example, those stemming from the implementation of projects where the association between action and impact are both visible and measurable, the links between sectoral and macro-economic policy and the sustainability of the natural resource environment have received much less attention. In analysing the impact of any policy change, policy makers would ideally take into account these environmental costs and benefits as well as the economic outcomes in terms of increased efficiency. Often, however, it is difficult even to determine whether the impacts associated with such policy decisions will be positive or negative, let alone the magnitude of any effects. The assessment of the impacts of policy decisions on the natural resource environment has been hindered by the absence of a methodology for tracing these impacts (Munasinghe and Cruz, 1994). Even where such relationships are recognised they are rarely incorporated at the policy design stage, as it is difficult to determine the magnitude of environmental effects until they become apparent. In the context of substantive economic policy reform, this implies that these effects are not taken into consideration until after the reform process has been implemented and the response is reactive rather than proactive. The latter point highlights the importance of both the environmental impact assessment and the analysis of the economic costs and benefits of policy decisions being completed at the same point in the policy formulation process, a theme central to the Action Impact Matrix (AIM) methodology (Munasinghe and Cruz, 1994). One problem facing researchers is that their attempts to incorporate sustainability issues into policy analysis are likely to be static in nature because both technology and population growth rates are considered to be exogenous influences. However, models that attempt to treat these variables endogenously are likely to be of little use to policy makers due to their being both highly complex and intractable, while more simplified models are likely to prove unreliable. This paper attempts to develop a framework that demonstrates that farmers’ supply response to changes in incentives resulting from, for example, changes in population density, is influenced by the existing state of technology and subject to the constraints on innovation imposed by government policy, and that this is likely to result in quite different site-specific outcomes. In explaining the AIM approach, Munasinghe and Cruz (1994) stress that the eventual impact of policy reform on the incentives faced by farming households is influenced by intervening institutional factors such as those affecting access to and use rights over resources, including land and water. Whilst they suggest that the complexity surrounding these issues implies that country-specific analysis will generally be required, the authors also point out that key reforms have specific identifiable impacts on subgroups of high priority problems. However, the AIM methodology does not appear to distinguish different possible responses to changes in incentives. In the agriculture sector, for example, output can be increased in response to an increase in output price by either extensifying or intensifying production activities, each having different associated impacts. It is therefore crucial to be able to identify the form and propensity to impact. For example, an appreciation in the exchange rate can result in inorganic inputs becoming relatively less expensive. Agricultural producers may respond by intensifying production on their more productive land. By contrast, a depreciation of the currency may result in greater amount of marginal land being brought into production as relative input prices rise. The impacts on the natural resource environment under each scenario might be quite different. In this paper we restrict our discussion to the effects on the natural resource environment which occur as a result of policy decisions that affect the agricultural sector. There has been substantive work carried out on the environmental impact of different farming practices, and on the development of technical indicators to describe the direction and magnitude of these impacts, notable among these being the OECD (1997) driving force-state-response framework. The OECD framework proceeds by attempting to determine what causes environmental conditions in agriculture to change, what effect this is having on the state of the environment, and what actions are being taken to respond to the changing state of the environment. The framework, thereby aids the identification of indicators to explain and quantify these links (OECD, 1999). However, there are still a number of limitations to the use of these approaches. Pearce (1999) concludes that although the OECD’s framework is valuable in terms of the development of indicators within a conceptual framework, there has been limited appreciation of the real driving forces such as missing markets and government policy. Pearce suggests that further research is needed to identify the non-market attributes of agriculture, namely, the external benefits and costs, and the relationship between government policy and the agriculture sector. Similar reservations are held by Doyle (1999) who finds that too many of the recommended OECD indicators are concerned with measuring the state of the environment in terms of physical and biophysical phenomenon. As such the indicators are not able to indicate the propensity of policy change to impact upon the state of the environment. One might conjecture that the reason why biophysical and technical indicators form the focus of attention is the difficulty of constructing indicators that reflect responses to policy change. Similarly, it could be that indicators are selected because of data availability, not any particular relevance to policy reform. Doyle states that ‘clear causal links between policy action and resource impacts need to be identified so that links between interpreting the indicators, developing appropriate policy responses and predicting the environmental and economic impacts can be clearly quantified.’ Thomassin (1999), however, relates that the current trend is the development of models in which policy influences production decisions in terms of crop mix and tillage practice. Results from such models are then used to estimate the environmental impacts with bio-physical models. This approach is taken in, for example, the Policy Evaluation Matrix and the Canadian Regional Agriculture Model. In line with the observations made in this section, we therefore focus on the ability of policy makers to determine the likely implications of policy reforms on changes in farming practice. In doing so we suggest a framework for considering the likely direction and magnitude of the impact of such changes on the natural resource environment.
نتیجه گیری انگلیسی
It is important for policy makers to realise that a specific policy change can have a wide array of potential impacts upon the natural resource environment, depending upon the wider policy context and the type of technology currently employed. As demonstrated with the South African application, it should be possible in a limited information scenario, to trace the likely response of farmers with regard to the practices used in agricultural activity, in order to achieve an a priori indication regarding which natural resource indicators should be monitored, and what complementary measures are likely to be necessary in order to offset any unacceptable environmental costs. Definitive conclusions in a study of this kind are hard to draw. The use of aggregate data often masks as much as it reveals. In addition, although South Africa is better placed than many countries, particularly other developing countries, with regard to the documented knowledge of environmental degradation, the lack of local (or even regional) estimates, and of time series data, makes correlation with specific agricultural policies little more than conjectural. The evidence suggests that such links can be made if routed through variables associated with farming practice, but the argument remains for the present largely circumstantial. It should be stressed, however, that the approach outlined in this paper is merely designed to preface more detailed research prior to policy implementation. In particular, more empirical investigation would be required to: (1) classify farming practices in terms of (a) the technology employed and (b) how close existing systems are to the margin; (2) determine the extent and trajectory of the environmental costs as represented by the shape of the cost curves. It is also apparent that in the analysis we have not attempted to incorporate the effects of conservation measures and the effects that extension and other environmental policy interventions might have in terms of the adoption of more sustainable farming practices. An extension of the above to incorporate such interventions into the analysis in a similar manner to that describing alternative economic policies would, however, be relatively straightforward.