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The term “integrated valuation” is defined and its relevance is discussed in terms of bridging the gap between cost-effectiveness analysis and economic valuation in the implementation of the European Union Water Framework Directive. We demonstrate how to integrate benefit valuation with the ecosystem services cascade framework using an Object-Oriented Bayesian Network (OOBN). The OOBN is then used to assess the benefits of nutrient abatement measures across a cascade of submodels of the driver-pressure-state-impact-response (DPSIR) chain for the Vanemfjord lake in Morsa catchment in south-eastern Norway. The lake is part of a complex lake system in a semi-urbanized catchment dominated by forest and agriculture. The catchment has highly variable seasonal climatic conditions affecting nutrient run-off and algal blooms. It has been one of the most eutrophic lakes in Norway with periodic cyanobacteria blooms, but continues to attract a large recreational user population, despite the large variations in water quality. The “DPSIR-OOBN” model is used as a case study of “integrated valuation” and evaluated for its applicability for decision support in nutrient abatement. We find that the DPSIR-OOBN model meets seven of the nine criteria we propose for “integrated valuation”. The model struggles to meet the criteria that ecological, social and economic values should be defined consistently in relation to impacts on lake quality. While the DPSIR-OOBN integrates from valuation methods across an ecosystem cascade to management alternatives, it is neither a full benefit-cost analysis, nor a multi-criteria analysis. However, we demonstrate how the DPSIR-OOBN can be used to explore issues of consistency in scaling and weighting of different ecological, social and economic values in the catchment system. Bayesian belief networks offer a consistent approach to analysing how management implementation probability may determine economic valuation. We discuss the implication of our integrated valuation not being able to account for farmer responses, in particular the incentive effects of the model not being able to predict abatement effectiveness and value. The resolution of the nutrient monitoring data and modeling technologies that were at our disposal are probably better in the Morsa catchment than for any other catchment of this size in Norway. We therefore conclude that using our integrated valuation model for assessing benefits of eutrophication abatement measures as part of the EU Water Framework Directive still lies in the realm of utopia – euphemistically speaking a “eutropia”. Financial support has been provided by the Research Council of Norway for the EUTROPIA project (grant no. 190028/S30), the European Union for the project FP6 AQUAMONEY (contract no. SSPI-022723), FP7 REFRESH (grant no. 244121) and the FP7 OpenNESS project (grant no. 308428).

De senere år har det blitt større oppmerksomhet rundt behovet for å redusere risikoen knyttet til tap av plantevernmidler fra punktkilder. Hovedmålet for prosjektet var å klarlegge behov og mulige metoder for å redusere forurensning fra punktkilder knyttet til bruk av plantevernmidler på norske gårdsbruk. Dette er søkt oppnådd ved en kartlegging av mulig forurensning fra punktkilder gjennom en spørreundersøkelse for å klarlegge bøndenes holdninger til problemstillingen, risikovurdering av forurensningen knyttet til aktuelle punktkilder samt en vurdering av mulige tiltak. Resultatene indikerer at over 50 % av bøndene i utvalget utførere utvendig vask av sprøyteutstyr på drenert areal uten kontroll på avløpsvannet, noe som i utgangspunktet bør oppfattes som en praksis som øker risikoen for punktkildeforurensning fra plantevernmidler. Modellsimuleringer med spredning av vaskevann på vegetasjonsdekt areal viser at denne anbefalte praksisen minker risikoen for forurensning. EUs direktiv for bærekraftig bruk av plantevernmidler setter fokus på behov for spesielle forurensningsreduserende tiltak i sårbare områder. Dette kan gi behov for økt fokus på punktkildeproblematikk bl.a. i nedslagsfelt til drikkevannskilder når disse bestemmelsene implementeres i ny norsk plantevernmiddelforskrift. I slike områder kan man dra nytte av eksisterende veiledningsmateriell for utforming av biobed og biofiltre for rensing av avløpsvann også i norsk landbruk. Vi vil anbefale at problemstillingen følges opp overfor norske bønder gjennom utarbeidelse av lettfattelige veiledninger og gjennomføring av informasjonskampanjer for å øke bøndenes oppmerksomhet for problemstillinger knyttet til plantevernmidler og punktkilder. Det bør videre vurderes om det er behov for spesielle tiltak i sårbare områder (drikkevannskilder).

A major criticism of contingent valuation methods relates to their hypothetical nature, where truthful revelation of willingness-to-pay is not secured because of the missing link between stated willingness-to-pay and the respondent's budget constraint. Large deviations between stated willingness-to-pay and actual payments in both field and experimental studies where actual payments were collected lend support to these claims. In its utmost format this lends contingent valuation studies vulnerable to strategic behavior. This paper combines the truthful revelation properties of multi-unit uniform price auctions and the median voter theorem to induce truthful revelation. The intuition is as follows : Let the price in the auction be set by the median bid, and pass this price onto a referendum. The expected net gains of passing the referendum would then be positive, implying that the budget constraint would be introduced in a probabilistic manner.

It is well known from the compliance literature that whenever it costly to monitor agents' compliance to contract terms, compliance is likely to be incomplete. This paper goes one step further by examining the implications of incomplete monitoring on agent's sales offers in auctions for environmental contracts. From a monitoring perspective we show allocation contracts to least cost also produces another gain – that less resources need to be spend on monitoring and enforcement. To get full use of this insight one needs to have auction procedures that provide incentives for truthful revelation of agents' private alternate incomes. Our second result is that the incentives for truthful revelation is lost when monitoring is incomplete unless the expected value of compliance exceeds the expected value of noncompliance. We demonstrate this result theoretically and through an economic experiment using an induced value reverse multi unit auction.

This paper addresses the coordination and innovation issues needed for promoting value added at the rural and regional level. There are two sides to value added: the ability to meet consumer demand, and to identify least cost ways of supplying the demanded goods. Human and social capital plays an important role on both sides. At the municipality level the supply side issues are complex. First, because the production space has far more dimensions than for the single entrepreneur. Second, because the value of some goods and services produced depend on what other goods and services those are available. On the supply side networks are important to solve the coordination issues, while networks for identifying and understanding consumer preferences are important on the demand side. Participation in these two network types compete for the same scarce resource, the time of the inhabitants of a municipality. We address these issues in more detail. A major insight from our work is that in addition to the time conflict, innovation and new information may make it more difficult to maintain coordination networks., paper from plenary session

Many previous analyses of multifunctionality in agriculture claim there is positive jointness between the production commodities and nonmarket goods and services. Those analyses have not treated acreage as an endogenous variable. This leads to several errors in terms of the analysis of policy impacts, and hence also the policy advice. The main contribution of this paper is the development of an analytical framework where acreage is endogenously determined. This framework is then used to demonstrate that any policy that influences resource allocation also affects agricultural commodity production. Hence, there is no such thing as a fully decoupled support. The impacts on commodity markets from direct payments to environmental goods and services are, however, smaller than the distortions from policies that inflate commodity prices.

Technical change at the farm level or changes in input prices often entail that the firm's supply function changes. These changes can take place in numerous ways. This paper presents a methodology that increases the consistency in supply responses across various sets of agricultural products and farm types with a market model based on a statistical response function approach. Since most farm simulation models are limited to a subset of regions and farm types, the linkage to an aggregated model requires a procedure for expanding these results to non sample regions, so that full regional coverage is achieved. This paper addresses theoretical aspects related to the consistency between micro and market level models. Next it deals with some empirical findings related to the selection of different functional forms for extrapolation. We conclude with a critical reflection on applicability of this method in addressing further needs on up-scaling of other economic as well as non-economic indicators.

Using market data, we have estimated the quasi option value of delaying to grow genetically modified corn and soybeans in Europe. We find that the current quasi option value of growing genetically modified soybeans and corn in Europe is high. This makes it likely that for the time being the information value of waiting exceeds the market gains of growing genetically modified plants in Europe.

The ongoing negotiations of the World Trade Organization (WTO) on agriculture has triggered an increased interest in the linkages between commodity production and public goods in agriculture. This paper takes a closer look at this issue. Particular emphasis is placed on production economics and policy design issues. Production is placed in a multi-product multi-input setting. The provision of public goods from agriculture is analyzed utilizing a principle-agent framework. Particular emphasis is placed on issues pertaining to incentive compatibility, informational aspects and the participation constraint. Public finance aspects of various policies are also addressed. Price supports are of particular interest as they are widely used in European agriculture. This makes analyses of price supports particularly relevant.

Browsing by large ungulate wildlife species damages valuable trees, but increases the productivity of the wildlife stock. The optimal management of trees and wildlife involves a tradeoff between these two impacts, as well as correct timing of tree harvests from each forest stand. For a model of moose and pine in Norway, it is found that harvest of adjacent forest stands should be adjusted over time to bring the stands into synch. Doing so maximizes the moose productivity boost that occurs immediately following harvest of mature trees, and protects the young trees from overbrowsing by spreading out the browsing pressure among more young trees. This result is, however, sensitive to the specification of the relationship between forage availability and moose productivity.

The increasing scarcity of Phosphorite poses a real threat to the world's food production. Global fertilizer production needs a steady phosphorus supply to cater to the global demand. Without Phosphorite, a new source of phosphorus must be found. As it stands today, no other substitute for Phosphorite fills the criteria of being both plentiful enough and economical to extract. Continued population growth and increased wealth also increase demand for both the amount and quality of food. This demand puts a considerable strain on the production of Phosphorite, leading to an over-extraction that is not compatible with economic theory regarding optimal use of non-renewable resources. Continuing the extraction and beneficiation of Phosphorite creates compounding negative externalities in the form of phosphogypsum stacks and wastewater. These stacks, comprised of toxic and radioactive materials, are incredibly harmful to the environment. Dike breaches and spills from wastewater reservoirs cause irreparable damage to nearby waters and soil and can potentially destroy whole ecosystems if left unmanaged. Because Phosphorite is ultimately used for food production, these negative externalities are under-prioritized over the potential crisis if food demand is not adequately met. Therefore, current environmental policies targeting Phosphorite have no significant bearing on its price. Today, the price of Phosphorite is dominated by short-term market behavior rather than longterm concerns of scarcity and the environment. This behavior is to be expected, as the production of Phosphorite is still increasing, meaning that a “peak phosphorus” situation is yet to occur. As long as the demand for food is met, there is no incentive to invest long-term, as doing so would lower funds available to invest for short-term profit. This situation will inevitably change once peak production has been reached. At this position, the demand for food can no longer be met, and it will ultimately be more profitable to invest in Phosphorite long-term, as its price must increase. Around 80% of phosphorus is lost through the journey from extraction to the dinner table. Increasing the efficiency of production and reducing losses is therefore paramount to ensure a longer lifetime of Phosphorite. A longer lifetime of the resource creates more opportunities for technological progress and new methods of production without the looming disaster of a food shortage. This can therefore potentially solve the scarcity problem of Phosphorite in the long run. M-ECON

Denne masteroppgaven undersøker tre forskjellige metoder for boligprisanalyse, der formålet er å utforske metodenes fordeler og ulemper. Da boligprisene i Oslo har steget mye og gjort det vanskelig for førstegangskjøpere å innta markedet, bruker studien typiske førstegangskjøperboliger som avgrensing i analysene. Metodene vi bruker er paneldataanalyse, tidsrekkeanalyse og prediksjon med regresjonsalgoritmer i maskinlæring. Alle analysene bruker et datasett med boligtransaksjoner i Oslo fra 2010-2020, som også inneholder attributter som beskriver hver solgte bolig. I paneldataanalysen bruker vi en fast effekt-modell, som analyserer månedlig gjennomsnittlig kvadratmeterpris for Oslos 15 bydeler. Med få og nøye utvalgte uavhengige variabler kan vi trekke frem hvordan disse har en fast effekt på gjennomsnittlig kvadratmeterpris. Resultatene viser at økning i boliglånsrenten har en effekt som reduserer kvadratmeterprisen. Videre ser vi at antall kvadratmeter har en negativ effekt, som vil si at man betaler mer per kvadratmeter for mindre boliger. Vi ser også at betydningen av å ha et adskilt soverom i førstegangskjøperboliger, hever kvadratmeterprisen. I tidsrekkeanalysen tester vi ut en SARIMA-modell, som predikerer gjennomsnittlig kvadratmeterpris i Oslo aggregert på månedsnivå. Her tester vi ut to modeller som predikerer på data vi allerede har. Resultatene her gir lovende resultater som vi videre bruker i en prognosemodell. Prognosen vi lager er på 1,5 år frem i tid, og viser at kvadratmeterprisene kommer til å øke mye. SARIMA-modellen vår gir gode resultater, men har svakheter når det kommer til starttidspunkt for prediksjon. Modellen ser ut til å følge trenden til datapunktene rundt starttidspunktet. Til sist ser vi på hvordan maskinlæring kan benyttes til å predikere boligpriser ved hjelp av tre-baserte regresjonsalgoritmer. Her ser vi på hvor nærme faktisk kvadratmeterpris modellen estimerer, gitt ett utvalg variabler som beskriver hver boligenhet samt boliglånsrente. Vi tester modellene Decision Trees, Random Forest og Extreme Gradient Boosting. Her oppnår sistnevnte best resultater med en RMSE på 3561,03. Videre benytter vi modellens funksjon som heter «Feature Importance» som et forsøk i å forklare hvordan modellen vekter variablene i prediksjonen. Hovedkonklusjonen fra denne studien er at metodene har både fordeler og ulemper. Metodene er vanskelige å sammenligne da de har forskjellige formål, men samlet belyser de ulike deler som er nyttige for førstegangskjøpere på jakt etter bolig. This thesis investigates three different methods for analyzing house prices. The aim is to explore which pros and cons the methods have, and how they contribute to analyzing the real estate market in Oslo. House prices has grown rapidly the ten last years, and smaller dwellings for a typical first-time buyer have become far more expensive than most can afford. Therefore, this thesis uses typical first-time buyer dwellings as a limitation in the analysis. The methods used are fixed effects in a panel data analysis, the univariate timeseries model SARIMA, and prediction with the machine learning algorithms Decision Trees, Random Forest and Extreme Gradient Boosting. The three analysis methods use a dataset of transacted properties in Oslo form 2010-2020. The dataset contains attributes which describes each unit sold. The fixed effects-model in panel data has an advantage in explaining why the average square meter prices varies. The panel data analysis investigates the 15 districts of Oslo as crosssections and months as the time series. Results here show that an increase in mortgage rate, reduce the average square meter price and vice versa. Further on the analysis shows that smaller dwellings have a higher price per square meter, and that having a bedroom increases the square meter price. The time-series analysis with SARIMA makes two predictions with in-sample data that produces promising results which we use in an out of-sample forecast 1,5 years ahead in time. The results shows that the SARIMA-model can produce good predictions, but that the model has weaknesses when it comes to the starting point of the prediction. If the datapoints around the starting point has a clear trend the models seem to follow this trend. In the machine learning analysis, we predict the actual square meter price based on each dwelling´s attributes and the mortgage rate at the selling point. The XGBoost algorithm produced the best prediction with an RMSE of 3561,03. Further on we use XGBoost feature importance in an attempt to understand how the model weights the attributes in its predictions. The main conclusion drawn from this thesis is that each method has its pros and cons. The panel data analysis is useful in explaining what causes price differences. The time-series analysis is useful in forecasting prices in the future and understanding historical and seasonal variations of the price. And the prediction with machine learning is useful in predicting house prices with high precision but lacks insight in how and why the variables influence the price. The methods cannot be compared directly because of their different perspectives, but all their purposes are all useful for first-time buyers. M-ØA

Conservation auctions have received increased attention since the turn of the century. Improved management of forests for biodiversity is one of the areas where this has been the case. Previous studies have to my knowledge not accounted for the impact of timber price variability on forest owners’ bidding behavior. This thesis fills this void in the literature. Brazee and Mendelsohn (1988) showed that timber price fluctuations affect the timing of timber cutting as the forest owners' reservation prices for cutting increase under higher price variability. A basic insight from auction theory is that no bidder will submit a bid that makes him or her worse off. This is also the case for conservation contract auctions. I combine the above two insights to show that bids for forest conservation auctions will become higher under timber price variability compared to cases with timber prices without such fluctuations. M-ECON

This thesis examines the effects of implementing a carbon tax on the greenhouse sector in Norway, with a specific case study of Wiig Gartneri in Rogaland. The sector is currently exempt from such taxation under the reasoning that carbon dioxide, through photosynthesis, serves as a production input. However, carbon emissions greatly surpass what is required in production, and it is questionable if an exemption can be upheld indefinitely. Our first research question relates to a scenario where no cost-effective alternative exists, where we look at the elasticity of natural gas demand. By treating the carbon tax as a constant increase in the price of natural gas we find that short term price fluctuations do not affect Wiig Gartneri’s demand. Our remaining research questions relates to the profitability of three specific technologies we deem promising. These are solar thermal collectors, biogas and woodchip combustion. To examine these technologies, we rely of net present value analysis of energy related expenses over the lifetime of the project and measure the results against a reference scenario. Our findings show that, despite providing the lowest levelized cost of energy, solar thermal collectors are not a profitable option. This is because of the greenhouse dynamics, where the collectors primarily produce in the summer where carbon dioxide is most scarce. Further, we find that while not requiring any initial investments, biogas requires a substantial price drop for it to become cost competitive in the foreseeable future. Finally, despite providing energy at a higher per energy cost than natural gas, the only cost decreasing alternative for the greenhouse is woodchips. This is because the biomass, despite being regarded climate neutral, emits more carbon dioxide than natural gas per energy output. This technology therefore provides a double benefit for the greenhouse, as it decreases expenses regarding both liquid carbon and carbon tax. M-ECON

The failure of undervalue or unpriced externalities, such as emissions, have led us to an unsustainable path that threatens our environment, our overall welfare and economic growth. To make the transaction to a “green economy” as efficient as possible, could policymakers through the usage of policy instruments, increase prices on fossil fuels. However, previous attempts and studies show that there is a general public resistance over increasing prices on fossil fuels to a level that is coherent with the social cost. A key issue for policymakers is therefore to gain public acceptance over increased prices on polluting products. Environmental economists have for decades tried to create the “perfect” policy design that yields most benefits, to further gain public acceptance. However, as complexity of these policies have increased, more oppositional arguments and alternatives have emerged. This has made the process of implementing environmental policies a complex, and costly matter. The studies by Aasen et al (2019) outlines that the vast majority of the Norwegian population see climate change as a comprehensive issue, however that they do not want their own economy being affected by the “green shift”. The aim for this thesis is thus to present a proposal on how Norway could gain public acceptance over the implementation of a more ambitious green tax reform in the context of reaching their climate goals by 2030. Implementing a policy design where all the revenue is distributed back to the public, such as the “Carbon Fee Dividend” design, will eliminate many of these hurdles that have been correlative with the present tax system. Goulder (1995) argues that a revenue-neutral policy almost always will achieve the environmental objective, as well as being highly costsaving. The simplicity of the design, and the perceived ease to mobilize for such a tax scheme, indicate a rapid implementation process. Given scientists' increased concern over current insufficient emission reductions, suggest the need for fast implementation of effective policies. M-ØA