ECOLOGICAL ECONOMICS 62 (2007) 341– 351 available at www.sciencedirect.com www.elsevier.com/locate/ecolecon ANALYSIS The economic impacts of river rehabilitation: A regional Input–Output analysis C. Spörria, M. Borsukb,⁎, I. Petersc, P. Reicherta aSwiss Federal Institute of Aquatic Science and Technology (Eawag), PO Box 611, 8600 Dübendorf, Switzerland bDepartment of Biological Sciences, Dartmouth College, Hanover, NH 03755, USA cHafenCity University for Construction and Spatial Development, 21071 Hamburg, Germany ARTICLE INFO ABSTRACT Article history: We developed a model to predict the impacts of river rehabilitation activities on the local Received 28 June 2005 economy. The model is based on the Input–Output analysis technique and was applied to Received in revised form 29 June 2006 the planned rehabilitation project for the River Thur in northern Switzerland, along the 4 km Accepted 6 July 2006 stretch between the communities of Bürglen and Weinfelden. We estimated changes in Available online 23 August 2006 local employment and local economic output resulting from government spending on rehabilitation, associated changes in adjacent land use, and increased recreational activity. Keywords: Accounting for land use changes required a modification of the conventional Input–Output River rehabilitation analysis technique which should be of general interest. We accounted for uncertainty in the Economic impacts analysis data and in some of the model assumptions by using a probabilistic formulation and Regional Input–Output model propagating uncertainty through the model equations. As time-consuming local surveys Probabilistic Input–Output analysis were beyond the scope of this study, we used the Location Quotient non-survey technique to Model uncertainty construct the local technical coefficients from national data and local employment data. Location quotient method This implies that the model can be applied quite easily to a different study area in Switzerland as long as local employment data are available. For each CHF 1 million expenditure per year on rehabilitation activities in our study region, we estimate an extra 8 fulltime employment equivalents (standard deviation, σ=0.4 fte) and an increased output of CHF 1.4 million (σ=CHF 0.05 million). The low uncertainty of these estimates can be partly attributed to the structure of Input–Output analysis and partly to the fact that we estimated changes in the economic output, rather than output itself. In addition to the above impacts, we estimate that increased recreational use of the area will increase output by as much as CHF 0.17 million (σ=CHF 0.12 million) and employment by as much as 1.7 fulltime employment equivalents (σ=1.3 fte), depending on the specific rehabilitation option selected. © 2006 Elsevier B.V. All rights reserved. 1. Introduction flooding, reduce wetlands, and reclaim land for agriculture and other uses (Petts, 1989). However, the negative ecological Artificial river regulation was seen as beneficial for the last consequences of these large-scale engineering projects are two centuries. Rivers were straightened and constrained to limit now being recognized, and river rehabilitation is being ⁎ Corresponding author. Tel.: +1 603 646 2708; fax: +1 603 646 1541. E-mail address: [email protected] (M. Borsuk). 0921-8009/$ - see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.ecolecon.2006.07.001 342 ECOLOGICAL ECONOMICS 62 (2007) 341– 351 undertaken to partially undo the damage. Options for rehabil- effects such as changes in land use and recreational activity itation include river bed widening, construction of retention resulting from the modified riverscape. Our analysis uses a basins or side channels, reconversion of land to natural twelve sector Input–Output model (Leontief, 1936; for an floodplain, or instream habitat improvements. Sound decision- authoritative textbook, see Miller and Blair, 1985) to assess making about which specific rehabilitation activities to imple- the sector-specific changes in local production and employ- ment requires the consideration of many factors. In addition to ment resulting from these two influences. Input–Output ecological quality and flood security, project stakeholders are models have been used previously to support environmental concerned about implementation costs, risks to local ground- and natural resource policy (e.g. Berck and Hoffmann, 2002), water, loss of existing land uses, changes in aesthetics and including recent use in multi-objective decision models (Cho, recreational opportunities, and impacts on the local economy, 1999; Oliveira and Antunes, 2004). However, to our knowledge, especially employment in agriculture (Hostmann et al., 2005a). their use as one component of a multi-criteria decision The Rhone–Thur Project (http://www.rhone-thur.eawag.ch, analysis incorporating uncertainty has not yet been reported. Peter et al., 2005) was initiated by several Swiss research This paper is structured as follows: First we provide an institutes and federal agencies to provide support for river overview of the Input–Output method and how it is applied and rehabilitation decisions in Switzerland. The project uses the extended here as the basis for our analysis. We then describe framework of decision analysis, which is a structured method how we deal with uncertainty in model inputs and assump- for providing insight into situations with multiple alternatives, tions. This is followed by a description of our specific study area multiple conflicting objectives, and uncertainty in the decision and the relevant data. Model results for this study area are then outcome (Clemen, 2001; Eisenführ and Weber, 2003). There are presented, followed by a discussion of these results and the two, relatively independent parts to decision analysis as method in general. Finally, we offer some conclusions. applied to environmental management: (1) a predictive part focused on probabilistically estimating the consequences of decision alternatives, expressed in terms of attributes charac- 2. Methods terizing stakeholder goals (or “objectives”), and (2) a valuation part focused on determining how stakeholders value the 2.1. Input–Output analysis accomplishment of objectives and how they weigh the relative importance of different objectives. For each alternative, the Input–Output analysis (henceforth I–O analysis) is based on a two parts are then brought together to calculate the mathe- detailed accounting of the transactions (measured in mone- matical expected value of stakeholder preferences. The alter- tary units) between different sectors of the economy: the natives can then be ranked according to these expected values. producing sectors (also called industries), the household An overview of how this process applies to river rehabilitation, sector, the government sector and the rest of the world with particular emphasis on the generation of probabilistic (ROW). This accounting is represented by an Input–Output outcome predictions, is provided by Reichert et al. (in press).A table (Fig. 1). Input flows are recorded in the columns of the detailed description of the stakeholder valuation process is table, and outputs are recorded in the rows. At the heart of the given by Hostmann et al. (2005a,b). Input–Output table is the interindustry transactions table — a Scientific prediction of outcomes generally involves the use matrix of transactions between the producing sectors. These of integrated models, which in the case of river rehabilitation interindustry transactions are also called “intermediate de- include submodels of hydraulics, morphology, ecology, and mand.” The household, government and ROW sectors exercise the economy (Reichert et al., in press). We have found “final demand.” The household sector provides “value added” probability networks (Pearl, 1988) to be a useful model struc- input (in the form of capital and labor) to the producing sector ture for combining different types of submodels that explicitly and receives interest and wages. consider prediction uncertainty using probability distributions The typical application of I–O analysis is the calculation of (e.g. Borsuk et al., 2004). A probability network consists of both economic impacts (changes in industry output, employment, a graph structure and a probabilistic description of the and income) resulting from exogenous changes in final relationships among variables in a system. The graph structure demand. (By “exogenous changes” we mean changes not represents cause-and-effect assumptions that allow the com- explained within the model, but assumed to be given.) An plex causal chain linking initial actions to final outcomes to be example would be an assessment of the job creation due to the factored into sets of relationships that can be constructed initiation of a public works project in a region. independently of the rest of the network. In the Rhone–Thur As in Fig. 1, let x represent the vector of the industry Project, these sets represent the various submodels, all of outputs, y the vector of final demand, and Z the matrix of which will eventually be integrated as a single probability inter-industry transactions.1 The relationship between these network (Reichert et al., in press). In the present paper, we is then, describe the design and the development of the economics 0 1 submodel. As probability networks use conditional probability 1 @ A distributions to describe the relationships between variables, x ¼ Z ⫶ þ y ð1Þ representation of uncertainty