Predator–prey analysis using system dynamics: An application to the steel industry

In this paper, we use a predator–prey model to simulate intersectoral dynamics, with the global steel sector as the prey that supplies inputs and the automotive sector as the predator that demands its inputs. A further prey, an additional upstream supply sector, namely the iron ore sector, is added to reflect the implications of scarcity and resource limitations for industrial development and economic prospects. We find that capacity constraints in the steel industry could limit the future supply of vehicles, a result exacerbated by the unsustainable use of iron ore reserves. The solution is not for marginal steel industries to close, but for steelmakers to adapt and move to less resource-demanding secondary steelmaking technology rather than focusing on primary steelmaking. The forecasting capabilities of the model are compared with the outputs from a neural-network model. Although the results are comparable over the short term (±10 years), over the long term, results diverge, showing that forecasting steel-industry dynamics is complex and that further work is required to disentangle the drivers of supply and demand. This study indicates the potential advantages of using predator–prey models in modelling the supply chain in economics.http://www.sajems.org/am2016Economic

A dynamic, stochastic, Bayesian, provincial input–output model for the South African economy

Background: The green economy has long been important in public discourses, as has been the forecasting of macroeconomic phenomena. Aim: The purpose with this study was to construct a regional input–output model for the South African economy. Setting: The model is a coupled input–output/system dynamics model. It is dynamic in the sense that sector growth follows the ‘limits to growth’ hypothesis. The model is used to explore the impact on the green economy, and also on poverty indices. Method: It was constructed using Vensim®, a system dynamics modelling package. Bayesian methods were utilised to estimate realistic values for the multipliers. Type I multipliers for output, income, employment and gross value added (GVA) are estimated. The model was then ‘tested’ by forecasting various resource sectors’ GVA (i.e. agriculture, mining, water, electricity). Results: The model fits the historical data well, replicating provincial GVA as well as national GVA to an acceptable standard. The multipliers fell within appropriate ranges, and followed a priori expectations. Conclusion: The model provides ‘highly accurate’ forecasts of various macroeconomic parameters, including the resources sectors. The impact of different multipliers in the three resources sectors on various poverty indices in South Africa was also assessed. Contribution: The model has great potential for further use in the agricultural, energy and resource sectors, but also has wider application since it provides a means for generating an input– output table for any specific year based on the forecasting of input – output elements

Does a reduction in the price of rhino horn prevent poaching?

Rhino poaching around the world has increased inordinately, to the extent that concerns exist over the possible survival of the species. An open access rhino poaching model is developed for South African rhino. The model is a hybrid dynamical model, as both a system dynamics model as well as a Bayesian network model are developed. The system dynamics model is used to estimate the unknown parameter values (through optimisation) and also to determine the intervals for the parameters. These intervals are then used in the Bayesian Belief Network model to assess uncertainty. Hybrid approaches improve the ability to validate models compared with conventional modelling. The resultant model indicates that reducing the price of rhino horn would not be effective at curbing poaching, unless poacher costs are also increased. However, increasing poacher costs is not a realistic policy option since these costs are largely beyond the control of decision-makers. The insensitivity of price to poaching effort has implications for methods proposed to reduce the value of rhinos, such as introducing synthetic rhino horn and the de-horning of rhinos.The National Research Foundation and DEA: NRM.http://www.elsevier.de/jnc2018-09-30hj2017Economic

Debunking the myth that a legal trade will solve the rhino horn crisis : a system dynamics model for market demand

There is considerable debate in the literature over whether or not to legalise the trade in rhino horns. Here a system dynamics model is developed that considers five components: rhino abundance, rhino demand, a price model, an income model and a supply model. The results indicate that income elasticities are much greater than previously observed, while demand is relatively insensitive to price. At the same time, legalising the trade without income modification policies did not prevent extinction. The theory of s-curve growth may provide some indications of future growth patterns of Asian economies. Results suggest that, even though the demand curve for rhino horn may be downward sloping as conventional theory predicts, non-conventional demand management strategies may be more effective than price orientated demand curve strategies such as trade legalisation in curbing supply.National Research Foundation (NRF) and Economic Research Southern Africa (ERSA).http://www.journals.elsevier.com/journal-for-nature-conservation/2016-11-30hb2016Economic

A categorisation and evaluation of rhino management policies

Rhino populations are at a critical level and new approaches are needed to ensure their survival. This study conducts a review and categorisation of policies for the management of rhinos. Twenty seven policies are identified and classified into in situ (reserve based) and ex situ (market based) policies. The policies are then evaluated based on four target areas: poachers/hunters; consumers; intermediaries and the game reserves themselves. The study finds that protected areas management policies seem most beneficial in the short run, in particular the enforcement of private property rights over resource utilisation, as well as the establishment of wildlife sanctuaries that act as sustainable breeding grounds for rhino populations.The National Research Foundation and Economic Research Southern Africa.http://www.tandfonline.com/loi/cdsa202017-12-01hb2016Economic

Predator–prey analysis using system dynamics : an application to the steel industry

In this paper, we use a predator–prey model to simulate intersectoral dynamics, with the global steel sector as the prey that supplies inputs and the automotive sector as the predator that demands its inputs. A further prey, an additional upstream supply sector, namely the iron ore sector, is added to reflect the implications of scarcity and resource limitations for industrial development and economic prospects. We find that capacity constraints in the steel industry could limit the future supply of vehicles, a result exacerbated by the unsustainable use of iron ore reserves. The solution is not for marginal steel industries to close, but for steelmakers to adapt and move to less resource-demanding secondary steelmaking technology rather than focusing on primary steelmaking. The forecasting capabilities of the model are compared with the outputs from a neural-network model. Although the results are comparable over the short term (±10 years), over the long term, results diverge, showing that forecasting steel-industry dynamics is complex and that further work is required to disentangle the drivers of supply and demand. This study indicates the potential advantages of using predator–prey models in modelling the supply chain in economics.http://www.sajems.org/am2016Economic

Predator–prey analysis using system dynamics : an application to the steel industry

In this paper, we use a predator–prey model to simulate intersectoral dynamics, with the global steel sector as the prey that supplies inputs and the automotive sector as the predator that demands its inputs. A further prey, an additional upstream supply sector, namely the iron ore sector, is added to reflect the implications of scarcity and resource limitations for industrial development and economic prospects. We find that capacity constraints in the steel industry could limit the future supply of vehicles, a result exacerbated by the unsustainable use of iron ore reserves. The solution is not for marginal steel industries to close, but for steelmakers to adapt and move to less resource-demanding secondary steelmaking technology rather than focusing on primary steelmaking. The forecasting capabilities of the model are compared with the outputs from a neural-network model. Although the results are comparable over the short term (±10 years), over the long term, results diverge, showing that forecasting steel-industry dynamics is complex and that further work is required to disentangle the drivers of supply and demand. This study indicates the potential advantages of using predator–prey models in modelling the supply chain in economics.http://www.sajems.org/am2016Economic

An overview of salient factors, relationships and values to support integrated energy-economic systems dynamic modeling

Integrated energy-economic modelling is needed to support the development of energy and climate policies. This study asserts that it is important to consider a system dynamics modelling approach that includes dynamics, endogenous treatment of uncertainty and risks, and both aggregate economic and disaggregate technical or engineering levels of analysis. The study examined the economic growth and the factors of production, elasticities, macro- and technical substitutability; energy cost shares, heat engine efficiencies and energy services efficiencies. Emphasis was laid on the support of the future development of integrated energy-economic models covering (a) the key factors or components; (b) the relationships among these components; (c) a quantification of parameters; and (d) the implications for the development of an integrated energy-economic system dynamics model. The study suggested the following: a non-linear relationship in production and consumption; large variations among price and income elasticity values across time frames, across countries and regions, and across energy goods; a far from perfect substitution among factors of production and among energy goods on a macro-level; technical/engineering limits to substitution on a micro-level; and engineering and behavioural limits on what can be achieved with increased efficiencies. The study argues that integrated energy-economic modelling intensifies the accounting for the factors, relationships, quantifications, and implications, and that this practice allows for such models to describe a complex, emergent energy-economic reality that informs better energy policy

An overview of salient factors, relationships and values to support integrated energy-economic systems dynamic modeling

Integrated energy-economic modelling is needed to support the development of energy and climate policies. This study asserts that it is important to consider a system dynamics modelling approach that includes dynamics, endogenous treatment of uncertainty and risks, and both aggregate economic and disaggregate technical or engineering levels of analysis. The study examined the economic growth and the factors of production, elasticities, macro- and technical substitutability; energy cost shares, heat engine efficiencies and energy services efficiencies. Emphasis was laid on the support of the future development of integrated energy-economic models covering (a) the key factors or components; (b) the relationships among these components; (c) a quantification of parameters; and (d) the implications for the development of an integrated energy-economic system dynamics model. The study suggested the following: a non-linear relationship in production and consumption; large variations among price and income elasticity values across time frames, across countries and regions, and across energy goods; a far from perfect substitution among factors of production and among energy goods on a macro-level; technical/engineering limits to substitution on a micro-level; and engineering and behavioural limits on what can be achieved with increased efficiencies. The study argues that integrated energy-economic modelling intensifies the accounting for the factors, relationships, quantifications, and implications, and that this practice allows for such models to describe a complex, emergent energy-economic reality that informs better energy policy

Financing active restoration in South Africa : an evaluation of different institutional models

The restoration of natural capital is increasingly becoming important to counter ongoing land degradation. The Natural Resource Management programme of the Department of Environmental Affairs (DEA: NRM) has long been investing in options to improve the effectiveness of active restoration. The aim of this study is to conduct a cost-benefit analysis of two approaches to active restoration at selected sites in KwaZulu-Natal, South Africa. This study compares a barter approach to a financial compensation approach, both of which are used to finance and advance active restoration. The barter system relies on community members to grow various tree seedlings, and they then receive various goods in exchange for the seedlings grown, whereas the financial compensation sources the seedlings from various commercial nurseries. We use a system dynamics model to evaluate the benefits and costs of these restoration approaches. The main finding is that restoration through the reintroduction of indigenous trees contributes a great deal towards increased carbon sequestration, with the barter option marginally cheaper than the nursery option. The model estimates an annual saving of more than R120 000 per annum with the barter approach in terms of the total restoration costs. However, the financial saving is not significant, as the model concludes that the financial compensation approach is more economically attractive considering a broader range of variables. The model estimated the value of water lost to be -R2 929 992.14 for the financial compensation model and -R2 920 412.76 for the barter financing model over 30 years. With the financial compensation model, the rate of clearance was found to be higher, thus translating directly into a greater accumulation of benefits. The lesser losses in water value, coupled with the higher gains in value-added products for the financial compensation model, are the main reason the financial compensation model is the more economically attractive financing approach.The Department of Environmental Affairs (DEA) and the Working for Water programme.http://www.aaae-africa.org/afjaream2018Economic