Adopting Organic Agriculture: An Investigation Using the Theory of Planned Behaviour

Certified organic production by small-scale farmers in developing countries is increasingly promoted as an opportunity to access a growing and dynamic market, while at the same time, enhance productivity and improve incomes. Nevertheless, adoption has been limited. The economics literature suggests profitability is the main constraint, however, the sustainable agriculture literature is inconclusive and considers attitudes of significant importance. Using the Theory of Planned Behaviour, this study investigates the psychological barriers to adoption using small-scale avocado producers from Michoacan, Mexico as a case study. The data is obtained from a household study carried out during 2004 and is modelled using an ordered probit model. Despite positive attitudes towards organic production, intentions to convert are negative. Intentions are significantly influenced by social pressures (subjective norm) and the perceived ability to successfully convert to organic production. Promotion of organic production will therefore require a focus on information asymmetries within the wider population, development of technical skills, the alleviation of credit constraints and the creation of an enabling environment.organic agriculture, theory of planned behaviour, entry barriers, O13, Production Economics, Q12, D8,

Energy Disaggregation for SMEs using Recurrence Quantification Analysis

Energy disaggregation determines the energy consumption of individual appliances from the total demand signal, which is recorded using a single monitoring device. There are varied approaches to this problem, which are applied to different settings. Here, we focus on small and medium enterprises (SMEs) and explore useful applications for energy disaggregation from the perspective of SMEs. More precisely, we use recurrence quantification analysis (RQA) of the aggregate and the individual device signals to create a two-dimensional map, which is an outlined region in a reduced information space that corresponds to ‘normal’ energy demand. Then, this map is used to monitor and control future energy consumption within the example business so to improve their energy efficiency practices. In particular, our proposed method is shown to detect when an appliance may be faulty and if an unexpected, additional device is in use

Adoption of Certified Organic Production: Evidence from Mexico

Adoption of organic production and subsequent entry into the organic market is examined using Mexican avocado producers as a case study. Probit analysis of a sample of 183 small-scale (<15ha) producers from Michoacán suggests that adoption is positively influenced by management and economic factors (e.g. production costs per hectare and making inputs), but also by social factors (e.g. membership of a producers’ association). Experience in agriculture has a significant but negative effect. Effective policy design must be therefore be aware of both the economic and social complexities surrounding adoption decisions

An innovation diffusion model of a local electricity network that is influenced by internal and external factors

Haynes et al. (1977) derived a nonlinear differential equation to determine the spread of innovations within a social network across space and time. This model depends upon the imitators and the innovators within the social system, where the imitators respond to internal influences, whilst the innovators react to external factors. Here, this differential equation is applied to simulate the uptake of a low-carbon technology (LCT) within a real local electricity network that is situated in the UK. This network comprises of many households that are assigned to certain feeders. Firstly, travelling wave solutions of Haynes’ model are used to predict adoption times as a function of the imitation and innovation influences. Then, the grid that represents the electricity network is created so that the finite element method (FEM) can be implemented. Next, innovation diffusion is modelled with Haynes’ equation and the FEM, where varying magnitudes of the internal and external pressures are imposed. Consequently, the impact of these model parameters is investigated. Moreover, LCT adoption trajectories at fixed feeder locations are calculated, which give a macroscopic understanding of the uptake behaviour at specific network sites. Lastly, the adoption of LCTs at a household level is examined, where microscopic and macroscopic approaches are combined

Electric vehicles and low-voltage grid: impact of uncontrolled demand side response

The authors are looking at the impact of electric vehicles (EV) charging from low-voltage (LV) networks. Based on the data obtained from two different pilot projects: (i) Mini-E trial where EV users were incentivised to charge during the night; (ii) My Electric Avenue trial, where there were no similar incentives, authors want to quantify the impact of EV charging, presuming that the number of home-charging EV users will increase significantly in the near future. By assuming that the current load at individual household level is known or inferred, simulations are performed to estimate the future load. The authors look at different percentages of EV uptake and model clustered scenarios, where the social networking effect is imposed – users adopt an EV with a higher probability if their neighbour already has one. Simulations demonstrate that incentivising night-time charging can create large new peaks during the night, which could have negative effects on LV networks. On the other hand, simulations based on the data with no incentives shows that naturally occurring diversity in charging behaviour does not automatically result in comparable network stress at the same penetrations

Motivations matter: Behavioural determinants of preferences for remote and unfamiliar environmental goods

Discrete choice experiments (DCE) are one of the main methods for the valuation of non-market environmental goods. However, concerns regarding the validity of choice responses obtained in such surveys remain, particularly in surveys dealing with environmental goods remote from and unfamiliar to respondents. This study assesses behavioural determinants of preferences for conservation benefits of a marine protected area on the Dogger Bank, a shallow sandbank in the southern North Sea in an attempt to assess construct validity of survey responses. The Theory of Planned Behavior (TPB) and the Norm Activation Model (NAM) are employed to empirically measure constructs that predict stated choices. The study finds that identified protest respondents score significantly lower on most TPB and NAM components than non-protesters. Results further show that components of both the TPB and the NAM robustly predict choice behaviour. The inclusion of the TPB components improves the predictive power of the estimation model more than the NAM components. In an additional latent class logit model, TPB and NAM components plausibly explain different patterns of WTP for conservation benefits of an offshore marine protected area. These findings support construct validity of stated choice data regarding the valuation of remote and unfamiliar environmental goods

Valuing conservation benefits of an offshore marine protected area

Increasing anthropogenic pressure in the offshore marine environment highlights the need for improved management and conservation of offshore ecosystems. This study scrutinises the applicability of a discrete choice experiment to value the expected benefits arising from the conservation of an offshore sandbank in UK waters. The valuation scenario refers to the UK part of the Dogger Bank, in the southern North Sea, and is based on real-world management options for fisheries, wind farms and marine protection currently under discussion for the site. It is assessed to what extent the general public perceive and value conservation benefits arising from an offshore marine protected area. The survey reveals support for marine conservation measures despite the general public's limited prior knowledge of current marine planning. Results further show significant values for an increase in species diversity, the protection of certain charismatic species and a restriction in the spread of invasive species across the site. Implications for policy and management with respect to commercial fishing, wind farm construction and nature conservation are discussed

The implications of energy systems for ecosystem services: A detailed case study of offshore wind

Globally, the deployment of offshore wind is expanding rapidly. An improved understanding of the economic, social and environmental impacts of this sector, and how they compare with those of other energy systems, is therefore necessary to support energy policy and planning decisions. The ecosystem services approach provides a more holistic perspective of socio-ecological systems than traditional environmental impact assessment. The approach also makes possible comparisons across disparate ecological communities because it considers the societal implications of ecological impacts rather than remaining focused on specific species or habitats. By reporting outcomes in societal terms, the approach also facilitates communication with decision makers and the evaluation of trade-offs. The impacts of offshore wind development on ecosystem services were assessed through a qualitative process of mapping the ecological and cultural parameters evaluated in 78 empirical studies onto the Common International Classification for Ecosystem Services (CICES) framework. The research demonstrates that a wide range of biophysical variables can be consistently mapped onto the CICES hierarchy, supporting development of the ecosystem service approach from a broad concept into an operational tool for impact assessment. However, to improve confidence in the outcomes, there remains a need for direct measurement of the impacts of offshore wind farms on ecosystem services and for standardised definitions of the assumptions made in linking ecological and cultural change to ecosystem service impacts. The process showed that offshore wind farms have mixed impacts across different ecosystem services, with negative effects on the seascape and the spread of non-native species, and positive effects on commercial fish and shellfish, potentially of most significance. The work also highlighted the need for a better understanding of long term and population level effects of offshore wind farms on species and habitats, and how these are placed in the context of other pressures on the marine environment

Modelling the demand and uncertainty of low voltage networks and the effect of non-domestic consumers

The increasing use and spread of low carbon technologies are expected to cause new patterns in electric demand and set novel challenges to a distribution network operator (DNO). In this study, we build upon a recently introduced method, called 'buddying', which simulates low voltage (LV) networks of both residential and non-domestic (e.g. shops, offices, schools, hospitals, etc.) customers through optimisation (via a genetic algorithm) of demands based on limited monitored and customer data. The algorithm assigns a limited but diverse number of monitored households (the 'buddies') to the unmonitored customers on a network. We study and compare two algorithms, one where substation monitoring data is available and a second where no substation information is used. Despite the roll out of monitoring equipment at domestic properties and/or substations, less data is available for commercial customers. This study focuses on substations with commercial customers most of which have no monitored 'buddy', in which case a profile must be created. Due to the volatile nature of the low voltage networks, uncertainty bounds are crucial for operational purposes. We introduce and demonstrate two techniques for modelling the confidence bounds on the modelled LV networks. The first method uses probabilistic forecast methods based on substation monitoring; the second only uses a simple bootstrap of the sample of monitored customers but has the advantage of not requiring monitoring at the substation. These modelling tools, buddying and uncertainty bounds, can give further insight to a DNO to better plan and manage the network when limited information is available