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Environmental Policy and Endogenous Technical Change: A Theoretical & Empirical Analysis

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Objectives/Hypotheses: This project will develop a deeper understanding of relationship between productivity change and environmental policy that considers environmental inputs into the production process. A case study of OCS oil will be used to measure productivity change, accounting for environmental inputs. The study will compare engineering estimates of the costs of complying with environmental regulations to ex post performance, that includes innovative firm responses, like process change. The study will also estimate potential cost savings of policies that increase flexibility and encourage innovation.

Approach: Technical innovation will be modeled as a random process, where the likelihood of new discovery depends upon the level of research and development activities. The model will be linked to various types of environmental regulations to determine their implications for productivity growth, and the resulting costs of protecting environmental quality.

A detailed case study of the offshore oil industry will be carried out as an application of the framework. First, a detailed historical analysis of offshore oil will be developed to provide a time line for technical and environmental performance of the industry. Next, a model will be estimated and hypotheses regarding technical innovation will be tested. Estimates of productivity change will be decomposed to identify the significance of specific technical advances and more subtle changes in production processes. These estimates will be used to specify key parameters for alternative scenarios regarding the future of technical change in the offshore oil industry.

The estimated model will be used to simulate various alternative scenarios regarding production activities, productivity, and alternative environmental policies. Various policy questions will be examined, such as identifying potential cost savings that result from innovative pollution control measures, and the associated benefits that can be derived from flexible approaches that encourage innovation, including market based approaches for pollution control. Given the inherent uncertainties involved, the emphasis will be on developing a reasonable range of estimates and identifying critical parameters through the use of sensitivity analyses. This will also suggest data needed to develop more refined analyses.

Expected Results: Improved understanding of the role of environmental policy in productivity changes that will lead to improvements in decision making and the design of environmental regulations. A detailed case study provides a quantitative assessment of potential cost savings that indicates the significance of potential benefits of environmental policy that encourages innovation.

Metadata

EPA/NSF ID:
R826610
Principal Investigators:
Opaluch, James J.
Jin, Di
Grigalunas, Thomas A.
Technical Liaison:
Research Organization:
Rhode Island, University of
Funding Agency/Program:
EPA/ORD/Valuation
Grant Year:
1998
Project Period:
October 1, 1998 -to September 30, 2001
Cost to Funding Agency:
$325,000
Project Status Reports:
For the Year 1999:

Objective: This project will develop a deeper understanding of relationship between productivity change and environmental policy that considers environmental inputs into the production process. A case study of OCS oil will be used to measure productivity change, accounting for environmental inputs. The study will compare engineering estimates of the costs of complying with environmental regulations to ex post performance, that includes innovative firm responses, like process change. The study also will estimate potential cost savings of policies that increase flexibility and encourage innovation.

Progress Summary: The first year of the project has involved four efforts: a literature review, conceptual modeling, data collection and identifying a historical timelines. Each of these is discussed below.

Literature Review. We have carried out a careful literature review of recent papers on endogenous technical change. We have identified several new and highly relevant papers that were published since our proposal. The advances embodied in these papers have been incorporated into our conceptual modeling, which is discussed in the next section.

Conceptual Modeling. We have developed a simplified model of technical progress as a random process, where the time to discovery depends upon investments in research and development activities. Also, the "size" of the discovery is a random variable, where size is taken as an indicator of the degree productivity change. A "large" discovery means that the cost of producing a given output is decreased substantially, or viewed from the dual perspective, the quantity produced increases substantially for a given set of inputs. Similarly, a "small" discovery will lead to a small reduction in the cost of producing a given level of output, or the quantity produced increases marginally with output held fixed.

This framework allows us a potential test for our proposed hypothesis of technical progress as a series of minor changes in production processes, versus the discovery of discrete new technologies. One would expect that if the primary source of technical progress is subtle changes in production processes and "learning by doing," that technical progress should be dominated by frequent, but marginal discoveries. In contrast, if the primary source of technical progress is major discoveries, then technical progress should appear as infrequent, but major jumps in productivity.

This should show up as we fit our model to the data. We propose to estimate a technical progress production function by estimating parameters of the two statistical distributions for technical change: a count data model (e.g., a poisson or negative binomial) of new discoveries, plus a continuous distribution (e.g., a gamma distribution) on the degree of technical progress embodied in a particular discovery. Given these two estimated distributions, we can provide a measure of the extent to which technical progress is driven primarily by a relatively small number of new discoveries, or large discrete jumps in technology, versus a multitude of refinements, or minor incremental steps forward.

Several unresolved issues still remain to be explored, including: (1) the empirical specification and estimation of the production function for technical change; and (2) the design of a test or a measure that gets at the issue of many small refinements, versus a few large discoveries. More fundamentally, we need to identify possible interactions between the two types of advances. For example, there could be a small number of large technical advances spread over a significant period of time. The new discovery in itself might not engender a huge leap forward, but might be subject to a series of refinements over the intervening period. However, refinements of an existing technology might have diminishing returns. In this case, each successive new discovery not only results in a direct improvement in technology, but also opens the door to a series of technical refinements, which would not have been possible without the new advance. For example, when the automobile was initially invented, it did not give rise to a major advance in capabilities beyond the horse. However, refinements in the automobile have progressed to the point where the horse is no longer a reasonable substitute for an automobile. At the same time, the refinements that made the automobile superior are not at all applicable to improving transportation technology based on the horse. So, it may be that the new discovery engenders a major improvement in productivity only because of refinements, and at the same time the refinements are possible only because of the new discovery. This may imply that it is difficult, and perhaps not at all meaningful, to distinguish between technical development in the form of new discoveries, versus refinements of technology.

Data Collection. We have identified and obtained a substantial amount of data. We have collected outputs and inputs for each well in the Gulf of Mexico over the period 1947 to 1995. This is a huge data set. We have determined that the most sensible unit of production for OCS oil is the field level. We have been developing SAS programs to aggregate the data to the field level. The objective of this effort is to develop a new database for our estimation needs and our later simulations. We also are considering whether analysis should more appropriately focus on production of oil and gas from given fields, or whether the proper unit of analysis is discovery of fields. We plan to pursue both of these avenues to identify the more conceptually appropriate and empirically promising.

Historic Timelines. The objective is to construct a time profile of environmental regulatory activities and on identifiable changes in technology. These timelines will provide important inputs into our empirical analysis. For example, a timeline on discrete, identifiable new technologies will allow us to relate technical progress to particular new discoveries.

We have developed a draft timeline of environmental regulations on OCS oil production by reviewing some of our past work?Federal Register, API reports, ICF reports, and online legal database. We have identified several studies of OCS production technologies, including environmental technologies, and are in the process of developing a timeline on new discoveries.

Future Activities: In the next year, we will complete data collection and further develop the theoretical model.

Supplemental Keywords: petroleum, productivity, innovative technology, economics, technical change, offshore oil.

For the Year 2000:

Objective: The objectives of this project are to:

  • Develop a deeper understanding of the relationship between technical change and alternative environmental policies that accounts for environmental inputs and depletion of natural capital stocks.
  • Use a case study to measure historic rates of technical change, accounting for environmental inputs.
  • Compare ex ante, engineering estimates of the costs of complying with environmental regulations to actual, ex post performance that includes innovative means of achieving standards, like process change.
  • Estimate benefits of environmental policies that provide increased flexibility and that encourage innovation.
  • Simulate the long run, relationship between productivity change and environmental protection.

Progress Summary: The majority of the accomplishments fall under four general tasks: (1) data collection and management, (2) creation of indexes for new technological discoveries, (3) measurement of technical change, and (4) the theoretical model of technical change with complementary technologies. Progress on each of these tasks is briefly discussed below.

Data Collection and Management. Over the past year, we have largely completed our task of data collection, and have expended considerable efforts cleaning, merging, and otherwise preparing the data for analysis. We have collected data for production of oil and natural gas at the well level for the Gulf of Mexico from 1948 through 1998. This is a truly huge data set. We aggregated the data to the field level, as we believe that production efficiency is most appropriately measured in terms of production from each field. Considerable effort was required to identify, collect, clean, and merge this production data with other data, including estimates of total resources, changes in resource estimates over time due to new discoveries, drilling distance for exploratory wells, drilling distance for development wells, etc. We also have linked various geophysical measures for fields, such as water depth, porosity, etc.

Creation of Indexes for New Technological Discoveries. One important goal of our project is to separate out technical change associated with specific technical discoveries with the more routine "learning by doing" (e.g., Arrow, 1962). This also relates to the two primary models of endogenous technical change?Romer's model, which looks at the creation of new technologies, and Aghion's model, which looks at incremental improvements in the quality of existing technologies.

We use an index of technical discoveries developed by Moss (1993), and used by Cuddington and Moss (2000) to measure technological inputs. Thus, the coefficient on this "input" is meant to capture the effect of discrete new technological discoveries, while the residual measure of technical change can be attributed to an accumulation of more modest improvements in production technology, attributable to "learning by doing," for example. We have updated Moss' index through 1998 using the same methodology. We then apply this technology index as a measure of technological change attributable to new technological discoveries.

Measurement of Technical Change. With the data largely in place, we have begun to carry out analyses of efficiency with these data. We completed a preliminary application of Data Envelopment Analysis (e.g., Fare, Grosskopf, et al., 1994) to construct efficiency measures. Simultaneously, we focused our efforts on two alternative methods?stochastic efficiency frontiers (e.g., Aigner, Lovell, et al., 1977) and more usual regression techniques, such as ordinary least squares and two stage least squares.

The Stochastic Efficiency Frontier (SEF) approach employs econometric methods to estimate efficient production frontiers using an asymmetric error term, rather than the usual econometric approach of estimating parameters that represent "average" (or representative) levels. Thus, the goal is to estimate parameters of the efficient frontier, or the maximum levels of production with a given set of inputs. The methods can be applied to estimating primal functions, such as the production function, or dual functions, such as the profit or cost function.

The SEF model is based on decomposing the random error term, e, into two parts?v and u. The component v is a normal error component that captures the usual sources of random variation (e.g., measurement error). This symmetric component of the error term also measures shifts that might be associated with random fluctuations in the efficient frontier. For example, in agriculture, random fluctuations in weather imply that the maximum amount of yield that comes from a given set of inputs will vary randomly. During years of bad weather, the maximum yield will be lower than it will be during years of average weather or years of ideal growing weather. The second component of the error term, u, is an asymmetric component that is a one-sided error term (e.g., a truncated normal or an exponential distribution), which measure deviations from the production frontier that arise due to errors in maximizing. So, a firm might use too high (or too low) a labor-capital ratio, or a firm might drill too many wells (or not enough wells) in its search for oil on a specific structure. A divergence from the optimal in either direction leads to a reduction relative to the efficiency frontier, hence the one-directional error component.

Maximum likelihood methods are used to estimate the coefficients of the model, including the variances of the two error components. This provides two pieces of information. First, the estimated coefficients provide an estimate of the efficiency frontier that can be used to construct the Malmquist indexes of productivity change. Second, the relative sizes of the two error variances indicate the extent to which inefficiency (distance from the efficient frontier) is an important factor in the data. The greater the variance of the asymmetric component, u, the greater is the estimated inefficiency of some observations relative to others. As the error variance on u goes to zero, inefficiency goes to zero, and all firms are on the efficient frontier. In this case, all random deviations are shifts in the frontier or measurement error.

We have completed our initial estimation of the Stochastic Efficiency Frontiers and have some preliminary results. However, additional analyses must be conducted before we can report any results or conclusions. The next steps in this process are to: (1) more fully specify the models, and (2) construct estimates of efficiency and technical change.

In the mean time, we are refining our models to complete our DEA work. We anticipate that our analysis of Stochastic Efficiency Frontiers and Data Envelopment Analysis will be largely completed over the next few months. This analysis will provide our Malmquist indexes of technical change and efficiency change, which are major components of the empirical work of our project. We also will use our technology indexes to distinguish between specific technical discoveries from more routine "learning by doing."

We also came to the realization that oil and gas prices might be important explanatory variables for efficiency. That is, in periods when prices are high, even low productivity fields may be profitable to exploit; when prices are low, the less productive fields would more likely be shut down. Thus, prices are likely to be important explanatory variables that are negatively related to the average and minimum efficiency of observed fields. We collected oil and gas prices over the time horizon, and integrated prices into our database.

Theoretical Model of Technical Change. We also have made progress in the theoretical model of technical change, focusing on complementary technologies, as discussed in previous progress reports. Production occurs at two levels. First, there are intermediate outputs, which are products of firms that make technical discoveries and hold the associated patent for that level of technology. Second, these intermediate outputs serve as inputs in the production of the final output, which is consumed.

The theoretical modeling of this process takes place in five steps:
1. Specify the technology for final output
2. Determine demand for intermediate outputs as a function of the current level of technology and output price.
3. Determine optimal pricing strategy for intermediate outputs by monopoly patent holder of technology for those intermediate outputs.
4. Determine expected profit function for patents holders.
5. Determine optimal investment in R&D for discovery of new technologies.

We have pursued two avenues for final output technology: a model of Hicks neutral change and a model of input-biased technical change. Both models are based on a Cobb-Douglas production technologies of the form:

where Y is the final output, Xij(t) represents the level of the intermediate outputs, and is the associated coefficient in the production function for final outputs. The model incorporates both the Romer approach, where technical change is embodied in discovery of new technologies, all of which operate simultaneously, and the Agion and Howitt approach, where productivity change represents incremental improvements in existing technologies, and the improvements replace the older methods (creative destruction). Within the context of our model, increases in n(t) represents discovery of new technologies, and increases in qij(t) represents incremental improvements in existing technologies.

Hicks neutral technical change is modeled by assuming the are constant, and Xij(t) = q(t)xij(t), where X is the "effective" input, including a technical change component, x is the actual physical level of the input, and q(t) represents the level of technology. Input biased technical change is modeled by assuming Xij(t) =xij(t), so that the effective input equals the physical input, but aij(t) = q(t), so that the exponent changes with new discoveries.

Both models demonstrate externalities in productivity, where advances in productivity for one input increase the productivity of other inputs. However, the theoretical results show that the monopolist owner of new technologies is not able to capture rents under Hicks neutral technical change, while at least part of the rent can be captured with input biased technical change, hence providing incentives for R&D activities.

Future Activities: The next steps in the research include completing each of the above tasks, and developing this work into a formal simulation model for the industry. Now that we have the OnFront software, we will complete the DEA analysis, followed by the Stochastic Efficiency Frontier analysis. These will be used to specify the production frontier and to characterize inefficiency within the industry. Next, we will measure technical and efficiency change in the offshore oil industry over the study period (1948-1998). Once this is complete, we will provide separate estimates of technical change for discrete new discoveries, versus refinements and "learning by doing." Finally, this will be used to specify our industry simulation model and to explore how alternative policies would affect technical change within the industry.

Journal Articles:

Aigner D, Lovell CAK, Schmidt P. Formulation and estimation of stochastic frontier production function models. Journal of Econometrics 1977;6(1):21-37.

Fare R, Grosskopf S, Norris M, Zhang Z. Productivity growth, technical progress, and efficiency change in industrialized countries. American Economic Review 1994;84(1):66-83.

Cuddington JT, Moss DL. Technical change, depletion and the U.S. petroleum industry: a new approach to measurement and estimation. American Economic Review 2000;90(5).

Arrow KJ. The economic implications of learning by doing. Review of Economic Studies 1962; 29(June):155-173.

Paper:

Moss DL. Measuring technical change in the petroleum industry: a new approach to assessing its effect on exploration and development. National Economic Research Associations 1993.

Supplemental Keywords: petroleum, productivity, innovative technology, economics, technical change, offshore oil.

For the Year 2001:

Objective: The objectives of this research project are to: (1) develop a deeper understanding of the relationship between technical change and alternative environmental policies that accounts for environmental inputs and depletion of natural capital stocks; (2) use a case study to measure historic rates of technical change, accounting for environmental inputs; (3) compare ex ante, engineering estimates of the costs of complying with environmental regulations to actual, ex post performance that includes innovative means of achieving standards such as process change; (4) estimate benefits of environmental policies that provide increased flexibility and that encourage innovation; and (5) simulate the long-run relationship between productivity change and environmental protection.

Progress Summary: Work on measuring and decomposing productivity change is nearing completion. The traditional issue of technological change has been recast by recognizing that production of market goods implicitly embodies joint production of market outputs and environmental commodities. Measures of productivity change should include both market and non-market outputs.

Data envelopment analysis (Charnes, et al., 1978; Fare, et al., 1985) has been applied to measure productivity change in the Gulf of Mexico offshore oil and gas production. This is an important application because energy resources are central to sustaining the economy, and because petroleum products currently are the key energy resources. For purposes of this project, a unique micro-level data set was developed that is comprised of the following: (1) production data, including monthly oil, gas, and produced water outputs from every well in the Gulf of Mexico from 1947 to 1998?the data include a total of 5,064,843 observations for 28,946 production wells; (2) borehole data describing drilling activity of each of 37,075 wells drilled from 1947 to 1998; (3) platform data with information on each of 5,997 platforms, including substructures, from 1947 to 1998; (4) field reserve data, including oil and gas reserve sizes and the discovery year of each of 957 fields from 1947 to 1997; and (5) reservoir-level porosity information from 1974 to 2000?the data include a total of 15,939 porosity measurements from 390 fields.

It has been determined that efficiency analysis is better focused at the field level rather than the well level, so the production data were aggregated into 18,117 observations on annual production from 933 fields over 50 years and used for the analyses described below. The first product of the project focuses on the relative sizes of depletion effects and technological progress for offshore oil production in the Gulf of Mexico, using the field-level data set described above. Previous innovation measures (Cuddington and Moss, 2001) were updated and adapted. The Cuddington and Moss index is based on a simple count of innovations. A National Petroleum Council industry survey of technology needs (NPC, 1995) was used to create an importance-weighted index.

The study supports the hypothesis that technological progress has mitigated depletion effects over the study period, but the pattern differs from the conventional wisdom for non-renewable resource industries. Contrary to the usual assumptions of monotonic changes in productivity or an inverted "U" shaped pattern, productivity declined for the first 30 years of the study period. More recently, the rapid pace of technological change has outpaced depletion, and productivity has increased rapidly, particularly in the most recent 5 years of the study period. This is consistent with reports of accelerating technological progress in recent years (Bohi, 1998).

A more thorough understanding of different components of technological change and depletion also is provided. It has been determined that both diffusion and "learning by doing" play more important roles in productivity change than innovations based on specific new discoveries. Early in the time series, field size is a more important determinant of productivity effects of depletion; later in the time series, water depth is more important. This is consistent with findings of large fields in increasingly deep waters in recent years.

To study finds of new fields, data were aggregated over all fields to calculate total annual production in the Gulf of Mexico. A yield per unit effort (YPE) model was employed that relates discoveries to the aggregate "effort" placed in exploration. With an increasing pace of technological change, net productivity has been increasing in recent years; however, recent productivity increases have not overcome depletion-related productivity declines of the earlier years (late 1940s through the 1970s).

The Porter hypothesis was examined, and a revised version was tested. The Porter hypothesis states that strict environmental regulations can induce firms to innovate and become more efficient, ultimately contributing to productive efficiency and profitability. The Porter hypothesis was recast to include joint market and non-market outputs (Repetto, 1996). Thus, productive efficiency is measured by taking into account not only market outputs, but also pollution levels that affect the supply of environmental commodities.

The results show a long-run upward trend in the productivity of environmental technologies. The results also support a recast version of the Porter hypothesis, where efficiency is measured with respect to joint products comprised of vectors of market and environmental commodities. Of course, the causality between innovation and stringency of environmental regulations could go in either direction. More stringent environmental regulations could result in innovation to overcome increased costs, or technological innovation could result in more stringent technology-based environmental regulations. Granger causality tests were used to determine the direction of causality between innovation and environmental regulation. Although there are no firm conclusions of the causal relationship between environmental stringency and technological innovation (new inventions), a clear causal direction was found from environmental stringency to less structural aspects of innovation, such as "learning by doing."

The final paper compares alternative measures of technological change to assess whether some proxies perform better than others in offshore oil and gas industry. The use of patents, weighted patents, direct innovation counting, and weighted innovation counting were compared. The initial analysis finds that all of the proxies fit relatively well as an approximation in cumulative case, but none of the alternative innovation indexes appears to be clearly superior to the others. Cumulative innovation indexes appear much more promising than incremental innovation indexes as a proxy measure of innovation. This suggests that there may be a problem of using proxy measurement for short-term analysis.

Future Activities: The next step in the research is to develop an industry simulation model to simulate alternative futures of the industry. Historic data will be used to simulate evolution of the industry to date. The future of the offshore oil industry will be simulated under alternative assumptions regarding future production activities, parameters regarding technical change, and alternative environmental policies. Various policy issues will be addressed, such as identifying potential benefits from innovative pollution control measures, and the associated benefits of that can be derived from flexible approaches, such as market-based approaches for pollution control. Given the inherent uncertainties involved, the emphasis is on developing a reasonable range of benefits that might result and identifying critical parameters through the use of sensitivity analyses. Additional data are needed to develop more refined analyses.

Supplemental Keywords: petroleum, productivity, innovative technology, economics, technical change, offshore oil, Porter hypothesis.

Project Reports:
Final
Objective:
The objectives of this research project were to: (1) develop a deeper understanding of the relationship between technical change and alternative environmental policies that accounts for environmental inputs and depletion of natural capital stocks; (2) use a case study to measure historic rates of technical change, accounting for environmental inputs; (3) compare
ex ante, engineering estimates of the costs of complying with environmental regulations to actual, ex post performance that includes innovative means of achieving standards, similar to process change; (4) estimate benefits of environmental policies that provide increased flexibility and encourage innovation; and (5) simulate the long-term relationship between productivity change and environmental protection.

Summary/Accomplishments:
In this report, we provide a more fundamental understanding of productivity change, where productivity is broadly defined to include both market and environmental outputs. This allows one to identify potential gains from flexible environmental regulations within a dynamic context, where technology changes over time, and where environmental regulations can contribute to (or inhibit) development of new technologies. This extends previous economic studies, which consider the adverse economic impacts of regulations only in the context of a fixed set of production technologies.

Our approach also allows one to extend productivity measures to environmental outputs as well as market outputs. In contrast, traditional studies have measured the effects of environmental regulations on productivity by considering only the effects on market inputs and outputs. From a broader social perspective, the goal of environmental regulations is to change the mix of outputs by reducing undesirable outputs (pollution emissions) at the expense of reducing market outputs and/or increasing market inputs. These traditional studies of productivity focus only on measuring the effective cost of regulations, and not the associated environmental benefits that are achieved. An appropriate measure of the impact of regulations on productivity must consider both the costs associated with reductions in market outputs (and/or increase in inputs) and the associated environmental benefits that are achieved. We also had hoped to compare ex ante estimates of costs of complying with U.S. Environmental Protection Agency (EPA) regulations with ex post costs, but we were unable to obtain proprietary ex post cost measures.

The first steps in the project were to develop timelines for environmental regulations and for important new technologies. The list of technologies is augmented by carrying out an extensive analysis of technology announcements in industry publications, thereby extending and refining the work of Moss (1993). We further refined this index of new technologies using survey data developed by the National Petroleum Council (National Petroleum Council, 1995) on the importance of specific technologies. Together, these were used to construct an index of identifiable new technologies, which was used to decompose productivity change, as discussed below.

Simultaneously, we developed a conceptual model for endogenous technological change as a random process, whereby research and development expenditures increased the probability of making a discovery, and where technologies are complementary (Opaluch, 2000). Complementarity of new technologies is a common phenomenon, as new discoveries often make existing technologies more valuable. For example, there are important synergistic relationships between technologies such as improved computer processing power and advances in materials science. New materials improve computer processing power, which contributes to further advances in materials. Similarly, complementary relationships exist for computer components; faster hard drives and faster memory chips make faster computer processors even more productive. However, the existing literature models technologically advance as either pure substitutes, as in the Agion and Howitt (1992) vintage model, or as additively separable, as in the Romer (1990) model.

Next, we collected extensive data on inputs and outputs for outer continental shelf oil and gas production in the Gulf of Mexico. We constructed a unique micro-level data set, including the following: (1) production data, including monthly oil, gas, and produced water outputs from every well in the Gulf of Mexico from 1947 to 1998. The data include a total of 5,064,843 observations for 28,946 production wells; (2) borehole data describing drilling activity for each of 37,075 wells drilled from 1947 to 1998; (3) platform data with information on each of 5,997 platforms, including substructures from 1947 to 1998; (4) field reserve data, including oil and gas reserve sizes and discovery year of each of 957 fields from 1947 to 1997; and (5) reservoir-level porosity information from 1974 to 2000. These data include a total of 15,939 porosity measurements from 390 fields.

These data were used to measure productivity change and to decompose productivity change into various constituents to provide a more fundamental understanding of the process within the context of our case study. We recast the traditional issue of technological change by recognizing that production of market goods implicitly embodies joint production of market outputs and environmental commodities, so that our measures of productivity include both market and non-market outputs. In comparison, traditional measures of productivity change consider market outputs only.

Recent literature has suggested that it may be possible to develop environmental regulations that encourage innovation, such that regulations increase productivity of market outputs, while also providing environmental benefits. This is the so-called Porter hypothesis (e.g., Porter, 1991; Porter and van der Linde, 1995). Recent theoretical literature has confirmed that the Porter hypothesis is not necessarily inconsistent with economic theory of rational behavior by firms (e.g., Xepapadeas and Zeeuw 1999; Mohr, 2002). As part of our research, we provide the first true empirical test of the Porter hypothesis discussed below.

We apply Data Envelopment Analysis ([DEA]; e.g., Charnes, et al., 1978; Färe, et al., 1985) to measure Malmquist indices (e.g., Malmquist, 1953; Caves, et al., 1982a, 1982b) of productivity change in the offshore oil and gas production in the Gulf of Mexico. This is an important industry because energy resources are central to sustaining our economy and because petroleum products currently are the key energy resources. Furthermore, offshore oil production is a technology-intensive industry that embodies important tradeoffs between production of market outputs and associated adverse impacts on the environment.

The first product of our research (Managi, et al., 2002a) focuses on the relative sizes of depletion effects and technological progress for offshore oil production in the Gulf of Mexico using our unique field-level data set from 1947 to 1998. We update and adapt previous innovation measures (Cuddington and Moss, 2001). The Cuddington and Moss index is based on a simple count of innovations. We use an industry survey of technology needs by the National Petroleum Council (NPC) (NPC, 1995) to create an importance-weighted index.

The study supports the hypothesis that technological progress has mitigated depletion effects over the study period, but the pattern differs from the conventional wisdom for nonrenewable resource industries. Contrary to the usual assumptions of monotonic changes in productivity or an inverted "U" shaped pattern, we found that productivity declined for the first 30 years of our study period. More recently, the rapid pace of technological change has outpaced depletion and productivity has increased rapidly, particularly in the most recent 5 years of our study period. This is consistent with reports of accelerating technological progress in recent years (e.g., Bohi, 1998). The central role of technological change in maintaining economic viability of this industry underscores the importance of designing environmental policy so as to encourage (or at least not unduly inhibit) development and implementation of new technologies.

We also provide a more fundamental understanding of different components of technological change and depletion. We find that both diffusion and learning-by-doing play more important roles in productivity change than innovations based on specific new discoveries. We find that, early on in our time series, field size is a more important factor in limiting productivity, but that water depth is more important later. This is consistent with findings of very large fields in increasingly deep waters in recent years.

We work with an aggregate model of offshore production to study technological change in new discoveries in the Gulf of Mexico (Managi, et al., 2002b). We employ a yield per unit effort (YPE) model that relates discoveries to the aggregate "effort" placed in exploration. We again find an increasing pace of technological change over time, so that net productivity has been increasing in recent years. We also find that recent productivity increases have overcome depletion-related productivity declines of the earlier years (late 1940s through the 1970s).

Next, we rethink the Porter Hypothesis and test a revised version (Managi, et al., 2002c). The Porter Hypothesis states that strict environmental regulations can induce firms to innovate and become more efficient, ultimately contributing to productive efficiency and profitability in the long run. Thus, the Porter Hypothesis implies that well-designed environmental regulations can potentially provide a win-win solution, with increased profitability and reduced pollution. Recent theoretical literature has demonstrated that, because of market imperfections in technical innovation, the Porter Hypothesis is not necessarily inconsistent with rational behavior by firms.

We recast the Porter Hypothesis to measure efficiency with respect to joint production of market and nonmarket outputs (e.g., Repetto, 1996). Thus, productive efficiency is measured, taking into account not only market outputs, but also pollution levels that affect the supply of environmental commodities. Our results show a long-run upward trend of productivity in the environmental sector, despite increasing environmental stringency. Our results reject the standard Porter Hypothesis, which considers productivity of market outputs only. Our results support a recast version of the Porter Hypothesis, where efficiency is measured with respect to joint products comprised of vectors of market and environmental commodities.

The causality between innovation and stringency of environmental regulations could go in either direction. More stringent environmental regulations could result in innovation to meet new challenges. Alternatively, cost-reducing technological innovations could result in the implementation of more stringent technology-based environmental regulations. We use Granger causality tests to test the direction of causality between innovation and environmental regulation. Although we cannot come to any firm conclusions of the causal relationship between environmental stringency and technological innovation (new inventions), we find a clear causal direction from environmental stringency to less structural aspects of innovation, such as so-called "learning by doing." This implies that tougher environmental standards do not necessarily lead to new technological innovations, but they clearly lead to increased efficiency of operations as a result of learning from experience.

We also compare alternative indices of technological innovation (i.e., identifiable new technologies) to assess whether some proxies perform better than others in the offshore oil and gas industry (Managi, et al., 2002d). To do so, we compare use of patent counts, weighted patent counts, innovation counts, and weighted innovation counts to explain the Malmquist index calculated using DEA. Our initial analysis finds that all of proxies fit relatively well as an approximation in cumulative case, but none of the alternative innovation indices appear to be clearly superior to the others. Cumulative innovation indices appear much more promising than incremental innovation indices as a proxy measure of innovation. This suggests that there may be a problem of using proxy measurement for short-term analysis.

We also use stochastic production frontier analysis (SPF) (Aigner, Lovell, and Schmidt, 1977; Meeusen and van den Broeck, 1977) to assess technological change in the offshore oil and gas industry (Managi, et al., 2002e). Being a statistical technique, SPF can be used to construct confidence intervals and can validate the DEA results. Results of our SPF model suggest that the effect of technological change on the offshore oil and gas industry at the field level was substantial over the study period from 1947 to 1995. Because of technological progress, the negative effect of resource depletion on field-level production frontier has been declining over time. Similarly, the negative impact of water depth on the production frontier has been falling. The results reveal that environmental regulation had a significantly negative impact on offshore production, although such impact has been diminishing over time because of technological change and improvement in management.

We then develop a simulation model based on the disaggregated, field-level data discussed above (Managi, et al., 2002f). The model is used to forecast the future production and pollution of the offshore oil industry through 2050 under alternative assumptions regarding: (1) the rate of new resource discoveries; (2) the rate of technological change; (3) the stringency of environmental regulations; and (4) the form of environmental regulations (command and control versus flexible environmental regulations). Given the inherent uncertainties involved, the emphasis is on developing a reasonable range of benefits that might result in identifying critical parameters through the use of sensitivity analyses.

Historic data are used to simulate evolution of the industry to date. The model based on disaggregated field-level data is used to forecast production and pollution through the year 2050 under different scenarios regarding technical change, future resource discoveries, and alternative environmental policies. We address various policy questions, such as identifying potential benefits from innovative pollution control policies and the associated benefits that can be derived from flexible approaches, such as market-based approaches for pollution control. This improved understanding of the potential role of technology and environmental policy can provide policy-relevant information for designing and implementing sound environmental policies.

We use different scenarios to explore the significance of various factors in determining forecasts. The baseline scenario uses historic rates for technological change, the number of new field discoveries, and the change in the stringency of environmental regulations. The baseline scenario also assumes that environmental regulations will continue to be based on command-and-control.

In our baseline scenario, oil and gas production increases by approximately 1.5 percent per year until 2020, when a declining trend sets in. Pollution levels remain relatively constant until 2014, and start to gradually decrease thereafter.

Various scenarios are used to explore how results change with alternative assumptions regarding: (1) R&D expenditures and associated levels of technological change; (2) new reserve discoveries; (3) environmental regulations; and (4) flexible regulations in the Gulf of Mexico. The alternative scenarios are defined as follows:

1. R&D Expenditures and Technological Change. The high scenario for R&D assumes expenditures increase at 1 percent per year. The R&D low scenario assumes expenditures decrease at 1 percent per year. The levels of R&D expenditures then were used to forecast future rates of technological change, which in turn affect the future levels of production.

2. New Discoveries. The high scenario for new resource discovery follows the Energy Information Administration high price scenario for new discoveries. The low scenarios assume discoveries decrease linearly over time, ceasing altogether at dates ranging from 2015 to 2045.

3. Environmental Stringency. The high scenario for growth in the stringency environmental regulations assumes that stringency grows at the same rate as the historic decade with the highest rate of growth (1980s). The low scenario for environmental stringency assumes an equivalent decline in the rate of change in environmental stringency. That is, regulations continue to become more stringent, but at a slower rate.

4. Flexibility of Environmental Regulations. The high scenario for flexibility of environmental regulations assumes reductions in compliance costs following the estimates of Popp (2001). Two different assumptions are compared. The first assumes the Popp results are applicable to all fields, and the second assumes that the Popp results are applicable only to new fields, while old fields are assumed to be locked into historic technologies associated with past command-and-control regulations.

Technological change, as influenced by R&D expenditures (Alternative Scenario 1), had the greatest effect on production. The high scenario for technological change increased production (and pollution) by 189 percent compared to the baseline scenario. The stringency of environmental regulations (Alternative Scenario 3) had the smallest impact on production. The number of new discoveries also has significant impact in maintaining the long-term production. Flexible regulations (Alternative Scenario 4) applied to all fields, results in production and pollution of about 85 percent higher than baseline scenario in 2050. If flexible regulation is applied only to new fields, production and pollution increase around 20 percent higher than the baseline scenario.

Conclusions:
Technological change is an important determinant of future standards of living, particularly in a society facing natural resource depletion coupled with increasingly stringent environmental regulations. Therefore, the impacts of regulations on technological change should be a central concern in the design of environmental policy. However, regulatory requirements for assessing economic effects of environmental policies tend to be limited to static notions based on available technologies. A notable exception to this are so-called "technology-forcing" regulations, which set standards beyond current state of the art, to force development of new technologies (e.g., CAFÉ regulations).

Traditional arguments regarding the cost effectiveness of flexible environmental regulations are reinforced when the implications for technological change are considered. Indeed, our simulation results suggest implications of flexibility of regulations for technological change could be more important than the level of stringency. This means that "win-win" solutions can be attained by providing more stringent but more flexible regulations.

Benefits associated with technological change are primarily long term in nature, and short-term economic impacts can be very important to industry. This argues for long-term regulations with phased implementation, consistent with historic practices. An additional potential impediment is industry acceptability. Companies that benefit from new approaches to achieving environmental regulations may not be the incumbent market leaders at the time of the regulations. This may suggest that flexible regulations could provide opportunities for new entrants, increased competition, and changes in market leadership. This could imply an additional source of resistance by industry, as objections to regulations could be amplified by the fact that private costs to powerful incumbent firms exceed the social costs to industry as a whole. New research is necessary to test these hypotheses and to explore their implications for optimal regulatory design, which may need to consider the likelihood of obtaining political support for new regulations.

Our results, with respect to the Porter Hypothesis, are somewhat sobering. We find no support for the hypothesis that more strict environmental regulations spur innovation that leads to an increase in productivity with respect to market outputs. However, we do find evidence for a weaker version of the Porter Hypothesis, which indicates that more strict environmental regulations increase productivity of the joint production function for market and nonmarket outputs. The source of increased productivity is a goal for future research. Our results suggest that productivity increases are associated with learning obtained from experience with new environmental technologies, rather than with development of new technologies. This is as expected for an industry faced with command-and-control regulations.

An open empirical question for future research is whether the form of the environmental regulations in our particular case study has impaired industry’s ability to achieve potential productivity gains. That is, the Porter Hypothesis states that well-designed environmental regulations could spur innovation leading to increases in productivity of market goods. However, regulations in the offshore oil industry historically have been command-and-control. Therefore, an interesting hypothesis is whether it is the form of regulations that have impeded potential improvements in productivity. A cross-industry study might be useful in exploring this hypothesis, where the data include sectors with varying degrees of flexibility in achieving environmental regulations.

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Supplemental Keywords:
petroleum, productivity, innovative technology, economics, technical change, offshore oil, Porter Hypothesis. , Economic, Social, & Behavioral Science Research Program, RFA, Scientific Discipline, Economics, Economics & Decision Making, Engineering, decision-making, SIC 1300, benefits assessment, compliance costs, cost benefit, decision making, economic benefits, economic incentives, ecosystem valuation, empirical analysis, endogenous technical change, environmental policy, innovative pollution control, offshore oil, technical innovation


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