ERN: Natural Resource Economics (Topic)最新文献

{"title":"Natural Resources, Institutions and the Quality-adjusted Human Capital","authors":"Soran Mohtadi","doi":"10.2139/ssrn.3744787","DOIUrl":"https://doi.org/10.2139/ssrn.3744787","url":null,"abstract":"PurposeThe purpose of this paper is to investigate the resource rents–quality-adjusted human capital nexus and the impact of quality of institutions.Design/methodology/approachFor a large data set of 161 countries for the period 1996–2018 (yearly and 4-year periods), fixed effect estimation method is applied to investigate the impact of resource rents on quality-adjusted human capital and the role of quality of institutions on this relationship.FindingsThe paper found little evidence on the negative, significant and direct impact of total resource rents on quality-adjusted human capital. However, the results show that the negative effect of resource rents can be mediated by the quality of institutions. This result is robust to a long list of controls, different specifications and estimation techniques, as well as several robustness checks. Therefore, institutional quality seems to play a critical role in determining the indirect impact of natural resources on human capital. Moreover, the obtained results demonstrate that this resource adverse effect depends on the type of resource rents; in particular, high dependency on oil rents in developing countries appears to harm human capital.Research limitations/implicationsThe paper shows that it is not obvious that total resource rents decrease human capital and found that the coefficient is no longer significant in the two-way fixed effects model. However, the analysis has emphasized the crucial role of political institutions in this relationship and has shown that countries with higher quality of institutions make the most of their resource rents transiting to a better human capital environment. This result is found to be robust to a list of controls, different specifications and estimation techniques, as well as several robustness checks. In addition, we demonstrate that not all resources affect human capital in the same way and found that oil rents have a significant negative effect on human capital. This is an important distinction since several countries are blessing from oil rents. From this we conclude that the effect of natural resources on human capital varies across different types of commodities. On the other hand, the interaction between institutions and the sub-categories of resource rents shows that oil rents can increase human capital only in developing countries with higher quality of institutions (above the threshold). This result is also still hold while using alternative measures of political institutions.Practical implicationsThe results in this paper have important policy implications. In particular, results highlight important heterogeneities in the role resource rents to the economy. As international commodity prices have shown high volatility in recent years, it is important for policy makers to understand the rents. Rents which are the difference between the price of a commodity and the average cost of producing it can have different effects in the economy, including the human","PeriodicalId":436211,"journal":{"name":"ERN: Natural Resource Economics (Topic)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125645761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How Many km2 of Solar Panels in Spain and how much battery backup would it take to power Germany","authors":"Dr. Lars Schernikau, William Smith","doi":"10.2139/ssrn.3730155","DOIUrl":"https://doi.org/10.2139/ssrn.3730155","url":null,"abstract":"Germany is responsible for about 2% of global annual CO2 emissions from energy. To match Germany’s electricity demand (or over 15% of EU’s electricity demand) solely from solar photovoltaic panels located in Spain, about 7% of Spain would have to be covered with solar panels (~35.000 km2). Spain is the best-situated country in Europe for solar power, better in fact than India or (South) East Asia. The required Spanish solar park (PV-Spain) will have a total installed capacity of 2.000 GWp or almost 3x the 2020 installed solar capacity worldwide of 715 GW. In addition, backup storage capacity totaling about 45 TWh would be required. \u0000 \u0000To produce sufficient storage capacity from batteries using today’s leading technology would require the full output of 900 Tesla Gigafactories working at full capacity for one year, not counting the replacement of batteries every 20 years. For the entire European Union’s electricity demand, 6 times as much – about 40 % of Spain (~200.000 km2) – would be required, coupled with a battery capacity 6x higher. \u0000 \u0000To keep the Solar Park functioning just for Germany, PV panels would need to be replaced every 15 years, translating to an annual silicon requirement for the panels reaching close to 10% of current global production capacity (~135% for one-time setup). The silver requirement for modern PV panels powering Germany would translate to 30% of the annual global silver production (~450% for one-time setup). For the EU, essentially the entire annual global silicon production and 3x the annual global silver production would be required for replacement only. \u0000 \u0000There are currently not enough raw materials available for a battery backup. A 14-day battery storage solution for Germany would exceed the 2020 global battery production by a factor of 4 to 5x. To produce the required batteries for Germany alone (or over 15% of EU’s electricity demand) would require mining, transportation and processing of 0,4-0,8 billion tons of raw materials every year (7 to 13 billion tons for one-time setup), and 6x more for Europe. The raw materials required include lithium, copper, cobalt, nickel, graphite, rare earths & bauxite, coal, and iron ore for aluminum and steel. The 2020 global production of lithium, graphite anodes, cobalt or nickel would not nearly suffice by a multiple factor to produce the batteries for Germany alone.","PeriodicalId":436211,"journal":{"name":"ERN: Natural Resource Economics (Topic)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122492830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the Long-Term Coupling and Short-Term Decoupling of Crude Oil and Natural Gas Prices","authors":"Hayette Gatfaoui","doi":"10.2139/ssrn.3790370","DOIUrl":"https://doi.org/10.2139/ssrn.3790370","url":null,"abstract":"This article scrutinizes the relationship between the U.S. crude oil and natural gas prices. The relationship exhibits regime changes, which depend on technological, economic and geopolitical factors. We find that crude oil and natural gas prices decouple over two periods of about three years each (i.e. short-term decoupling) while they couple over four subsequent periods (i.e. long-term coupling during thirteen years approximately). During decoupling periods, crude oil and natural gas prices exhibit a negative correlation while the correlation becomes positive during coupling periods. Using linear Kalman filter, we allow for stochastic regression coefficients and heteroskedastic errors in measurement equation to appraise the time-varying relationship between crude oil and natural gas price changes. Such approach decomposes a global nonlinear relationship into period-specific linear linkages. Moreover, random parameters/volatility illustrate the uncertainty in energy costs and prices, and handle local nonlinearity. Incidentally, natural gas and crude oil price changes are non-significantly linked during decoupling periods. Our findings depict changes in the competition between oil price makers and takers, and the impact of technological improvements, including the shale gas revolution. They also open the door to possible short- and/or long-term hedging and arbitrage strategies between crude oil and natural gas.","PeriodicalId":436211,"journal":{"name":"ERN: Natural Resource Economics (Topic)","volume":"310 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127494637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structural Behavioral Models for Rights-Based Fisheries","authors":"M. Reimer, J. Abbott","doi":"10.2139/ssrn.3582502","DOIUrl":"https://doi.org/10.2139/ssrn.3582502","url":null,"abstract":"Rights-based management is prevalent in today's developed-world fisheries, yet spatiotemporal models of fishing behavior do not reflect such institutional settings. We develop a model of spatiotemporal fishing behavior that incorporates the dynamic and general equilibrium elements of catch-share fisheries. We propose an estimation strategy that is able to recover structural behavioral parameters through a nested fixed-point maximum likelihood procedure. We illustrate our modeling approach through a Monte Carlo analysis and demonstrate its importance for predicting out-of-sample counterfactual policies.","PeriodicalId":436211,"journal":{"name":"ERN: Natural Resource Economics (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124494672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stranded Fossil Fuel Reserves and Firm Value","authors":"Christina V. Atanasova, Eduardo S. Schwartz","doi":"10.3386/w26497","DOIUrl":"https://doi.org/10.3386/w26497","url":null,"abstract":"Do capital markets reflect the possibility that fossil fuel reserves may become “stranded assets” in the transition to a low carbon economy? We examine the relation between oil firms’ value and their proved reserves. Using a sample of 679 North American oil firms for the period 1999 to 2018, we document that while reserves are an important component of oil firm value, the growth of these reserves has a negative effect on firm value. This negative effect on value is stronger for oil producers with higher extraction costs. When we decompose total reserves into developed and undeveloped reserves, we show that the negative effect of reserves growth on value is due to firms growing their undeveloped oil reserves. Unlike developed, undeveloped reserves require major capital expenditures and longer time before they can be extracted. We also document that the negative effect is stronger for undeveloped oil reserves located in countries with strict climate policies. Our evidence is consistent with markets penalizing future investment in undeveloped reserves growth due to climate policy risk. High level of institutional ownership, stock market liquidity and analyst coverage do not change the negative effect of undeveloped reserves growth on firm value.","PeriodicalId":436211,"journal":{"name":"ERN: Natural Resource Economics (Topic)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125109347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Greed is Good: From Super-Harvest to Recovery in a Stochastic Predator-Prey System","authors":"Yuanming Ni, L. Sandal, S. Kvamsdal, Diwakar Poudel","doi":"10.2139/ssrn.3447879","DOIUrl":"https://doi.org/10.2139/ssrn.3447879","url":null,"abstract":"This paper demonstrates a predator-prey system of cod and capelin that confronts a possible scenario of prey extinction under the first-best policy in a stochastic world. We discover a novel ‘super-harvest’ phenomenon that the optimal harvest of the predator is even higher than the myopic policy, or the ‘greedy solution’, on part of the state space. This intrinsic attempt to harvest more predator to protect the prey is a critical evidence supporting the idea behind ‘greed is good’. We ban prey harvest and increase predator harvest in a designated state space area based on the optimal policy. Three heuristic recovery plans are generated following this principle. We employ stochastic simulations to analyse the probability of prey recovery and evaluate corresponding costs in terms of value loss percentage. We find that the alternative policies enhance prey recovery rates mostly around the area of 50% recovery probability under the optimal policy. When we scale up the predator harvest by 1.5, the prey recovery rate escalates for as much as 28% at a cost of 5% value loss. We establish two strategies: modest deviation from the optimal on a large area or intense measure on a small area. It seems more cost-effective to target the stock space with accuracy than to simply boost predator harvest when the aim is to achieve remarkable improvement of prey recovery probability.","PeriodicalId":436211,"journal":{"name":"ERN: Natural Resource Economics (Topic)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124501053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of Age-Structured Bioeconomic Model: Recruitment, Weight Gain and Environmental Effects","authors":"Yuanming Ni","doi":"10.2139/ssrn.3447873","DOIUrl":"https://doi.org/10.2139/ssrn.3447873","url":null,"abstract":"More and more fishery researchers begin to acknowledge that one-dimensional biomass models may omit key information when generating management guidelines. For the more complicated age-structured models, numerous parameters require a proper estimation or a reasonable assumption. In this paper, the effects of recruitment patterns and environmental impacts on the optimal exploitation of a fish population are investigated. Based on a discrete-time age-structured bioeconomic model of Northeast Atlantic mackerel, we introduce the mechanisms that generate 6 scenarios of the problem. Using the simplest scenario, optimizations are conducted under 8 different parameter combinations. Then, the problem is solved for each scenario and simulations are conducted with constant fishing mortalities. It is found that a higher environmental volatility leads to more net profits but with a lower probability of achieving the mean values. Any parameter combination that favours the older fish tends to lend itself to pulse fishing pattern. The simulations indicate that a constant fishing mortality around 0.06 performs the best. A comparison between the optimal and the historical harvest shows that for more than 70% of the time, the optimal exploitation precedes the historical one, leading to 43% higher net profit and 34% lower fishing cost.","PeriodicalId":436211,"journal":{"name":"ERN: Natural Resource Economics (Topic)","volume":"427 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133723776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Is Oil as a Financial Resource Productive?","authors":"Kui-wai Li, Tianyu Wang","doi":"10.2139/ssrn.3616526","DOIUrl":"https://doi.org/10.2139/ssrn.3616526","url":null,"abstract":"This paper uses oil resource and total factor productivity as the proxies for the nominal economy and real economy, respectively, to show how oil as a financial resource would impact on economic productivity. We analyze the effect of oil export and oil import on the TFP growth rate in the full sample covering 132 countries from 1970 to 2014. Both system-GMM and nonparametric empirical results show that the oil resource has a significant negative influence on productivity growth in long-run. The empirical findings show that a 1% increase in oil export would result in 1.18% decrease in the TFP growth rate and a 1% increase in oil importing would lead to 2.91% reduction in TFP growth. After an oil price shock, the TFP growth rate declined in the resource-poor countries. Similar findings are arrived when the globe economy in divided into three groups of countries (oil exporting only, oil importing only, and countries performed both export and import of oil). The results support the argument that oil-rich countries failed to use their abundant oil resource for long-run economic growth.","PeriodicalId":436211,"journal":{"name":"ERN: Natural Resource Economics (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128631781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal Harvest Responses to Environmental Forecasts Depend on Resource Knowledge and How It Can Be Used","authors":"Steve J Miller, A. Rassweiler, L. Dee, Kristin Kleisner, Tracey Mangin, R. Oliveros-Ramos, J. Tam, F. Chavez, Miguel Ñiquen, S. Lester, Merrick Burden, S. Gaines, C. Costello","doi":"10.1139/CJFAS-2018-0283","DOIUrl":"https://doi.org/10.1139/CJFAS-2018-0283","url":null,"abstract":"Managing natural resources under large-scale environmental fluctuations like the El Niño Southern Oscillation (ENSO) is likely to become increasingly important under climate change. Forecasts of environmental conditions are improving, but the best response to an unfavorable forecast remains unclear; many practitioners advocate reducing harvest as a more precautionary approach, while prior economic theory favors increasing harvest. Using logistic and age-structured fisheries models, we show that informational constraints — uncertain stock estimates and restrictions on harvest policies — play a central role in choosing how to respond to a forecasted shock. With perfect knowledge and no policy constraints, risk-neutral managers should increase harvest when a negative shock is forecast. However, informational constraints may drive the optimal response to a forecast of a negative shock toward or away from precaution. Precautionary forecast responses arise when informational constraints make the harvest policy insufficiently sensitive to the true resource status. In contrast, uncertainty about the stock size can lead to more aggressive forecast responses when stock dynamics are nonlinear and not all fish are susceptible to fishing.","PeriodicalId":436211,"journal":{"name":"ERN: Natural Resource Economics (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130559134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Creaming - And the Depletion of Resources: A Bayesian Data Analysis","authors":"J. Lillestøl, R. Sinding-Larsen","doi":"10.2139/ssrn.3072338","DOIUrl":"https://doi.org/10.2139/ssrn.3072338","url":null,"abstract":"This paper considers sampling in proportion to size from a partly unknown distribution. The applied context is the exploration for undiscovered resources, like oil accumulations in different deposits, where the most promising deposits are likely to be drilled first, based on some geologic size indicators (“creaming”). A Log-normal size model with exponentially decaying creaming factor turns out to have nice analytical features in this context, and fits well available data, as demonstrated in Lillestol and Sinding-Larsen (2017). This paper is a Bayesian follow-up, which provides posterior parameter densities and predictive densities of future discoveries, in the case of uninformative prior distributions. The theory is applied to the prediction of remaining petroleum accumulations to be found on the mature part of the Norwegian Continental Shelf.","PeriodicalId":436211,"journal":{"name":"ERN: Natural Resource Economics (Topic)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131210848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}