text:
\section{Introduction} \label{intro}

Large-scale energy system optimisation models are increasingly used to support the urgent task of planning for the energy transition \cite{susser_model_policymaking}. Most typically, to understand how to deploy new energy infrastructure (renewables, transmission, storage) to make energy systems fully carbon-neutral at the country or continental scale while keeping the economic cost for society as low as possible \cite{deng_review_2020}. However, as reported by real-world stakeholders \cite{susser_user_needs_complexity} and increasingly acknowledged in the literature \cite{decarolis_formalizing_2017,yue_review_2018}, the provision of a single solution that minimises total economic cost is of little use in practice, for a number of reasons. First, real-world decisions on the energy transition involve a multitude of stakeholders, including local communities, with many other concerns than the total economic cost \cite{price_implications_2020}. Second, cost, in a model, is nothing but a numerical value affected by uncertainty, for instance due to technology cost projections that need to be best-guessed when optimising for a long-term horizon \cite{ellenbeck_how_2019}. It is therefore problematic to concentrate on that configuration which ensures the minimum economic cost when some of the apparently more costly options might end up being just as or even more cost-effective after cost uncertainty is realised in practice. Third and final, the generation and communication of only a single, least-cost solution can create confusion between what is least-cost and what is possible, with dangerous consequences. For instance, it is common to hear claims that a given investment decision, say installing large amounts of bioenergy power supply, is `required' for a country's energy transition because it is featured in the least-cost solution \cite{brown_response_2018}. In practice however, strategies without bioenergy may exist within the cost uncertainty range of the least-cost solution .

As a solution to the pitfalls of economic optimisation, some have proposed introducing secondary objectives, such as the minimisation of CO$_2$ emissions \cite{prina_multi-objective_2020,pickering_quantifying_2021}. Yet, the alternatives obtained along a multi-objective Pareto front and its near-optimal region cannot ensure that the full range of possibilities gets captured. Other alternatives, driven by unmodelled (or even impossible-to-model) objectives, might also exist and be relevant for real-world discussion \cite{brill_use_1979}. To address the impossibility to model all that might matter in reality, often referred to as the structural uncertainty of the modelling process \cite{yue_review_2018}, approaches known as `modelling to generate alternatives', or MGA, have come to the forefront in recent years \cite{decarolis_mga_2011,price_modelling_2017,li_investment_2017,berntsen_ensuring_2017}. Conceptualised by \citeauthor{brill_use_1979} in 1979 \cite{brill_use_1979} and first applied to energy system models a decade ago \cite{decarolis_mga_2011}, the basic idea behind MGA is: first, to compute the least-cost solution as a starting point; and second, to change the objective of the problem to search for something as different as possible from the previously-found solution, while enforcing that cost does not increase too much compared to the minimum feasible system cost. The process (also known as the `Hop, Skip and Jump' algorithm) can be repeated indefinitely, each time updating the objective to search for something different from all the previously found feasible solutions.

Yet, MGA displays limitations when applied to sufficiently large models, such as state-of-the-art energy system optimisation models covering the whole of Europe at high temporal and spatial resolution and with many technological options \cite{pickering_diversity_2022,victoria_speed_2022}. The number of meaningful alternatives that can be generated for such models is \textit{de facto} infinite, and the conventional form of applying MGA often fails to represent diversity well, for two reasons. First, the `Hop, Skip and Jump' algorithm does not span the solution space evenly, leaving even feasible configurations with clear differences in the technology mix unexplored. Previous empirical work showed that configurations with very low or very high shares of a particular technology in the overall capacity mix, located near the `corners', or extremes, of the multi-dimensional decision space, are not found by conventional applications of MGA even when they exist -- that is, when they can be eventually found if the model objective is explicitly set to find them \cite{decarolis_temoa_2016,neumann_near-optimal_2021}. Second, to keep the problem computationally tractable, the search for something `as different as possible' is typically applied only to high-level variables of energy system models, such as the total installed capacity for each technology type, even within models of high spatial granularity \cite{neumann_near-optimal_2021}. This leads to the generation of `technologically-distinctive' feasible solutions, which rely relatively more on technologies that were minimally deployed in the least-cost solution, for instance deploying more solar than wind, at the system scale. However, real-world concerns, such as the social acceptance of new infrastructure, primarily call for alternatives in the way capacity is located at the sub-national scale, even for a fixed, agreed-upon mix of technologies at the national scale \cite{lombardi_policy_2020}. For instance, alternate ways of spatially distributing wind capacity across sub-national regions, for a roughly fixed amount of total wind capacity to be deployed in the system. While it is true that technologically-distinctive feasible solutions are naturally likely to entail different sub-national distributions of capacity as well \cite{neumann_broad_nodate}, this does not necessarily make up for having spatially-distinctive alternatives around a roughly fixed technology mix \cite{lombardi_policy_2020}. On the other hand, an explicit search for such `spatially-distinctive' solutions substantially enlarges the number of potentially interesting alternatives to generate and thereby the overall computational burden.

In response to such limitations, several recent advancements of MGA with application to energy system modelling have aimed at either spanning the solution space more evenly or explicitly looking for spatially-distinctive solutions, or at achieving both at once. Yet, as mentioned above, the amount of alternatives that may matter for state-of-the-art energy system models of large size is virtually infinite. Even attempts at mapping `all' alternatives \cite{pedersen_modeling_2021} or spanning the solution space as evenly as possible \cite{neumann_broad_nodate} can ultimately provide only a finite sample of solutions based on high-level technological diversity. Furthermore, providing a finite set of options leaves open the question of how many alternatives are enough, i.e. which alternatives are redundant and which ones are not.

Acknowledging this, we set out to empirically investigate the computational trade-offs among different possible approaches to the generation of alternatives within high-resolution energy system models. In particular, we test four different ways of generating alternatives within the SPORES method, an original development of MGA that we have presented in previous work \cite{lombardi_policy_2020}. The use of SPORES does not cover all possible formulations of MGA in the literature; but the high customisability of the SPORES workflow lends itself to tweaking the search towards either spatial or technological diversity explicitly, allowing us to explore the trade-offs between the two. Some of the tweaks of the search strategy that we consider do originate from other recent MGA advancements, but are applied here in a spatially-explicit way even when they were originally conceived for application to high-level variables only (see section \ref{methods}). With application to a model of the European power system with 97 nodes and 3-hour temporal resolution, we show that deciding what is redundant and what is not is far from trivial, and that there is a clear trade-off in the computational efficiency of MGA search strategies in highlighting technological versus spatial diversity of feasible solutions. Based on our empirical findings, we propose possible solutions for energy modellers to make their MGA approaches as computationally efficient and practically relevant as possible. We openly release on Zenodo \cite{lombardi_customised_2022} the model and the code used for the generation of results to foster transparency, repeatability and further developments \cite{pfenninger_importance_2017}.

The remainder of the paper is organised as follows. Section \ref{methods} presents the latest version of the SPORES workflow, how we tweak its parameters to approximate different search strategies, and the European power system model to which we apply our experiments. Section \ref{results} shows and discusses our findings in terms of the overall discovered decision space for each search strategy, the amount of generated alternatives that are likely relevant for real-world decisions, and differences in the resulting flexibility for spatially moving capacity. We conclude (section \ref{conclusion}) by discussing the implications of our findings for other modellers and policy decision support.

\section{Methods} \label{methods}

The following subsections present the latest version of the SPORES workflow (subsection \ref{methods-workflow}), the different search strategies that we use within the workflow for the analysis of computational trade-offs (\ref{methods-search-strat}), and the specific version of the Euro-Calliope model that we use as a case study (\ref{methods-eurocalliope}).

\subsection{SPORES workflow} \label{methods-workflow}

The SPORES workflow, summarised in Figure \ref{fig:spores_workflow} differs from conventional MGA in two key aspects.

First, it searches explicitly for spatially-distinctive options. Conventional MGA searches for a new solution as different as possible to the optimal one by assigning weights (penalties) to aggregate capacity variables proportional to their capacity deployment in the economically optimal solution. SPORES, instead, assigns such weights to spatially-explicit capacity variables. For instance, if wind generation is deployed in the cost-optimal solution, SPORES assigns different penalties to wind generation capacity in each location rather than penalising wind generation overall. This means that the search for something different might result in a configuration that has  as much wind generation capacity overall, but distributes it differently in space.

Second, it does so from multiple directions within the feasible, near-optimal solution space. This parallel search from multiple directions arises by anchoring the MGA algorithm to different extremes of the feasible near-optimal space, instead of just using the least-cost solution as the starting point of the search. These additional extremes are identified by explicitly minimising or maximising the system-wide deployment of specific technology-capacity decision variables, a strategy also employed in other recent work \cite{neumann_broad_nodate}. We detail the mathematical steps required to generate alternatives based on such a workflow in the following subsections.

\begin{figure}[H]
	\centering
	\includegraphics[width=1\textwidth]{figures/scheme_SPORES.pdf}
	\caption{Schematic representation of the SPORES workflow. The feasible, near-optimal solution space is systematically explored through parallel runs. A base run (top of the figure) searches for alternatives adopting a spatially-explicit version of MGA. Weights are assigned not only to system-wide technology-capacity decision variables but rather to each technology-in-location capacity decision variable. In parallel, several other runs (bottom of the figure) explore the decision space from different directions. These runs add to the search for spatially-explicit alternatives a secondary, technology-explicit objective: the system-wide minimisation (and, alternatively, maximisation) of a specific technology. This ensures that configurations with an extremely high or low deployment of specific technologies do not get lost while also ensuring that several spatially-distinctive configurations of technology deployment are provided around those.}
	\label{fig:spores_workflow}
\end{figure}

\subsubsection{Identification of the cost-optimal solution} \label{methods-spores-opt}

We identify the cost-optimal solution, or economic optimum of the problem, by minimising the total annualised system cost, as per Equation \ref{eq:cost_opt}.

\begin{equation} \label{eq:cost_opt}
	\begin{array}{*{20}{c}}
		{\min :cost\, = \sum\limits_j {\sum\limits_i {\left( {{c_{fix,ij}}x_{ij}^{cap} + \sum\limits_t {{c_{var,ij}}x_{t,ij}^{prod}} } \right)} } }\\
		{s.t.\;\;\matr{Ax} \le \matr{b}}\\
		{\,\,\,\;\;\matr{x} \ge 0,}
	\end{array}
\end{equation}

where $i$ and $j$ indicate the $i$-th technology type and the $j$-th location of the model; $x_{ij}^{cap}$ is the decision variable pertaining to the installed capacity of the $ij$-th location-technology pair; $x_{t,ij}^{prod}$ is the decision variable related to the power production of the $ij$-th location-technology pair as a function of time; $c_{fix,ij}$, $c_{var,ij}$ are, respectively, the annualised fixed and variable costs per each location-technology pair; $\matr{A}$, $\matr{b}$, are a matrix and a vector of coefficients that build all the physical constraints in combination with the vector $\matr{x}$ of all decision variables.

\subsubsection{Assignment of weights} \label{methods-spores-weight}

Having identified the mathematical optimum, we assign a strictly positive weight ($w_{ij}^n$) to the generation capacity decision variables (location-technology pairs) that are non-zero. This weight can be assigned using different approaches which result in different search strategies. In the first published application of SPORES \cite{lombardi_policy_2020}, we assigned weights based on the relative deployment of a technology in a given location compared to the maximum potential for deployment at that location ($x_{ij,max}^{cap}$). The newly-found weight is then summed to the weight obtained in the preceding iteration ($w_{ij}^{n - 1} $), for any iteration other than the first (Equation \ref{eq:weight}). Here, we also test three additional weight assignment methods (see subsection \ref{methods-search-strat}).

\begin{equation} \label{eq:weight}
	w_{ij}^n = w_{ij}^{n - 1} + \frac{{x_{ij}^{cap,n}}}{{x_{ij,max}^{cap}}}
\end{equation}

\subsubsection{Main batch of SPORES} \label{methods-spores-main}

We hence obtain a SPORE by minimising the sum of location-specific weighted capacity decision variables. At the same time, we constrain the total annualised system cost ($cos{t_n}$) to remain in a neighbourhood of the optimal cost ($cos{t_0}$), as per Equation \ref{eq:gen_spores}.

\begin{equation} \label{eq:gen_spores}
	\begin{array}{*{20}{c}}
		{\min \;Y = \sum\limits_j {\sum\limits_i {{w_{ij}}x_{ij}^{cap}} } }\\
		{s.t.\;\;cos{t_n} \le (1 + s) \cdot cos{t_0}}\\
		{\matr{Ax} \le \matr{b}}\\
		{\matr{x} \ge 0,}
	\end{array}
\end{equation}

where $s$ is the accepted cost relaxation (also known as cost slack). Such a new, MGA-like objective function makes the problem an $\epsilon$-constrained multi-objective optimisation: the minimisation of already-deployed location-technology pairs is the explicit objective and cost is the implicit one.

\subsubsection{Parallel batches of SPORES with secondary objectives} \label{methods-spores-parallel}

The workflow steps outlined in subsections \ref{methods-spores-opt} to \ref{methods-spores-main} can be as well seen as a spatially-explicit version of the `Hop, Skip and Jump' algorithm. Such an algorithm has been repeatedly shown to struggle with pushing the search for alternatives up to the extreme corners of the multi-dimensional decision space \cite{decarolis_temoa_2016,neumann_near-optimal_2021}. The incorporation of the spatial dimension further exacerbates the problem, as it multiplies the variables at stake and the different feasible system configurations. A spatially-explicit version of the algorithm will focus explicitly on spatial diversity and will further struggle to discover alternatives in which the mix of deployed technologies is radically different. For such reasons, SPORES foresee a further systematic exploration of the decision space from alternative directions in which technology diversity is handled explicitly. They do so by adding to the objective function a second explicit objective: the minimisation (and, alternatively, maximisation) of the capacity of a specific technology.

\begin{equation} \label{eq:extra_spores}
	\begin{array}{*{20}{c}}
		{\min\;(or \max) \;{Y_2} = a \cdot \sum\limits_j{ x_{\overline {ij} }^{cap}} + b \cdot \sum\limits_j {\sum\limits_i {{w_{ij}}x_{ij}^{cap}} } }\\
		{s.t.\;\;cos{t_n} \le (1 + s) \cdot cos{t_0}}\\
		{\matr{Ax} \le \matr{b}}\\
		{\matr{x} \ge 0,}
	\end{array}
\end{equation}

where $x_{\overline {ij}}$ is the capacity decision variable associated with the technology under minimisation (or maximisation), and $a$ and $b$ are the weights associated to the different components of the objective function (which can be customised at need, as discussed in \ref{methods-search-strat}). This configures the problem as a linearised multi-objective optimisation problem, with two explicit objectives parametrised by the coefficients $a$ and $b$ that add up to the implicit $\epsilon$-constrained cost objective. However, despite the mathematical formulation being multi-objective, the two explicit objectives do not aim to reflect any plausible real-world decision factors, which differentiates the approach from typical applications of multi-objective optimisation \cite{prina_multi-objective_2020}. The secondary objective is only needed here as a technical means to operate the spatially-explicit MGA search from alternate extremes of the decision space.

\subsection{Customised search strategies} \label{methods-search-strat}

The SPORES workflow presented throughout subsections \ref{methods-spores-opt} to \ref{methods-spores-parallel} lends itself to customisation. Both the weight-assignment method (Equation \ref{eq:weight}) and the relative strength of the two explicit objectives in the parallel batches of SPORES (Equation \ref{eq:extra_spores}) can be modified at need. Different weight-assignment methods may push the search relatively more on spatial versus technology diversity \cite{lombardi_policy_2020}. Similarly, changing the relative strength of the technology-explicit secondary objective in Equation \ref{eq:extra_spores} may allow the search to depart more or less from the identified extreme of the decision space. In this work, we test four different weight-assignment methods and two levels of relative strength for the technology-explicit secondary objective. In both cases, the aim is to test the outcomes, in terms of redundancy of generated solutions, of pushing the search more on technology versus spatial diversity, or viceversa.

\subsubsection{Alternative weight-assignment methods}

First, we consider the weight-assignment method outlined in Equation \ref{eq:weight}, which we hereafter refer to as the \textit{relative-deployment} method. We designed this method with a focus on spatial diversity in previous work \cite{lombardi_policy_2020}.

Second, we test the the so-called \textit{integer} method originally proposed by \citeauthor{brill_use_1979} \cite{brill_use_1979} and later applied to energy system modelling by \citeauthor{decarolis_mga_2011} \cite{decarolis_mga_2011}. Reported in Equation \ref{eq:weight-integer}, it is the simplest method, but has the potential drawback of many variables ending up with the same weight, hampering the search efficiency. We apply it here in a spatially-explicit way, assigning weights to location-technology pairs rather than to system-wide technology variables only.

\begin{equation} \label{eq:weight-integer}
	w_{ij}^n = w_{ij}^{n - 1} + k_{ij}, \;\; {with}	 \; k_{ij} =
	\begin{cases}
		100, \; if \; x_{ij} > c\\
		0, \; if \; x_{ij} \le c
	\end{cases}
\end{equation}

where $c$ is a constant threshold defined to avoid that even very marginal deployments of capacity receive a weight, which would otherwise entail the risk that almost all location-technology pairs receive a non-zero weight. It is also worth noting that the \textit{integer} weight amounts here to 100 based on the internal unit scaling of our model; a different scaling, say 1 or 10, may make more sense for a model with different units.

Third, we consider a \textit{random} method, in which weights have no rationale and are indeed assigned as random integer numbers (Equation \ref{eq:weight-random}). This method approximates the random MGA search proposed by other authors \cite{berntsen_ensuring_2017,sasse_regional_2020}. Unlike such previous work, though, we do not consider further degrees of randomisation in the objective function, for the sake of consistency with the other analysed methods, and we apply the random weights to spatially-explicit decision variables.

\begin{equation} \label{eq:weight-random}
	w_{ij}^n = w_{ij}^{n - 1} + r_{ij}, \;\; {with}	\; r_{ij} = U(0,100)
\end{equation}

where $U(0,100)$ is a random uniform distribution.

Fourth and final, we propose an original \textit{evolving-average} method (Equation \ref{eq:weight-evolving}). The idea of this method is to retain a more explicit memory of past iterations, compared to just having incremental weights. In such a way one can assign a weight to each location-technology pair based on the distance from the \textit{average} capacity deployed for that pair across all previously found feasible solutions (${\overline{x_{ij}}^{cap,n-1}}$), which is kept up to date -- in other words, it \textit{evolves}.

\begin{equation} \label{eq:weight-evolving}
	\begin{cases}
			w_{ij}^n = |\ffrac{ {{\overline{x_{ij}}^{cap,n-1}}} - {x_{ij}^{cap,n}} }{{\overline{x_{ij}}^{cap,n-1}}}|\\
		{{\overline{x_{ij}}^{cap,n-1}}} = \ffrac{\sum\limits_{n=1}^{n-1} x_{ij}^{cap,n}}{n-1}
	\end{cases}
\end{equation}

\subsubsection{Relative strength of spatial- and technology-explicit objectives} \label{methods-spores-secondary_obj}
The parallel batches of SPORES arising from technology-explicit extremes of the decision spaces can be customised by tweaking the parameters $a$ and $b$ of Equation \ref{eq:extra_spores}. By default, both parameters are set to a unitary value, which means they have the same relative strength. Here, we test the outcome of reducing the relative strength ($a$) to 0.1 of the technology-explicit secondary objective. We want to avoid obtaining, for a batch of SPORES generated around the minimisation of a certain technology, only configurations in which such a technology is fully minimised. A reduction of the relative strength of the technology lever may allow us to obtain configurations in which the given technology is only partially minimised, further improving the technology diversity of the generated alternatives. As for the case of the other customised parameters (see subsection \ref{methods-spores-weight}), the absolute numbers we adopt here for the $a$ and $b$ parameters make sense within the unit scaling of our model. Other absolute values might be more appropriate for a different unit scaling. However, since our analysis focuses on modifications of the parameters relative to one another, the outcomes remain generally valid.

\subsection{Energy system model and MGA setup} \label{methods-eurocalliope}

We apply our customisations of the SPORES method to a power system model of Europe comprising 34 countries and 97 nodes, or locations, across those. The model is based on the well-established open-source energy system modelling framework Calliope \cite{pfenninger_calliope_2018} and takes the name of Euro-Calliope. Originally conceived by \citeauthor{trondle_trade-offs_2020} \cite{trondle_trade-offs_2020}, the version we use here is based on a previous work of ours \cite{pickering_diversity_2022} in which we updated the initial (brownfield) electricity grid topology to mirror the most relevant real-world transmission constraints as identified by the e-Highway 2050 project. Besides various hydroelectric technologies whose capacity is assumed to remain constant, the model features seven main technologies for capacity expansion: roof-mounted and open-field solar photovoltaic, on-shore and off-shore wind, bioenergy power plants, and battery and hydrogen-to-power storage. Each of these is used as a basis for two parallel batches of SPORES (Equation \ref{eq:extra_spores}), one in which the deployment of the technology is maximised and one in which it is minimised, at the system level. Transmission technologies are also allowed to expand, and add up to the parallel batches of SPORES. We generate 10 alternatives for each parallel batch, for a total of 160 alternatives. These add up to the 50 alternatives generated within the main batch, leading to an overall 210 SPORES. For all SPORES, we adopt a slack cost of 10\%, in line with previous work \cite{lombardi_policy_2020,pickering_diversity_2022,neumann_near-optimal_2021}. We refer the readers to the Supplementary Methods for further details about the model and how it has been customised for this work.

Albeit the Euro-Calliope model version that we use in this work allows for the modelling of all energy sectors, we subset the analysis to the power sector alone, without considering the additional electricity demand arising from the likely electrification of sectors such as building heat and transport. In fact, the aim of the present work is to focus on the computational trade-offs between different approaches to the generation of alternative near-optimal solutions. Subsetting the analysis to the power sector alone allows us to test many approaches while keeping the computational effort feasible. It also allows readers to compare our findings with those of other recent MGA studies applied to energy system models of Europe \cite{neumann_near-optimal_2021,pedersen_modeling_2021,neumann_broad_nodate}, which are all grounded on a similar model set up. What is more, the analysis of the available decision space for the full decarbonisation of Europe including all energy-consuming sectors has already been undertaken using Euro-Calliope \cite{pickering_diversity_2022} and would therefore be redundant to repeat here.

\section{Results} \label{results}

We present our results for the reference case in which the relative strength of the two SPORES objectives is even (see subsection \ref{methods-spores-secondary_obj}), whilst we provide Supplementary Results for the case of a reduced strength of the secondary, technology-explicit objective. We start by analysing the `shape' of the overall discovered decision space across the four search strategies, discussing which decision space is most technologically diverse. Hence, we assess how many of the discovered alternatives, in each case, would match a plausible stakeholder interest, such as the limited concentration of onshore wind farms, discussing the efficiency at discovering spatial diversity across search strategies. Finally, we look for potentially real-world relevant spatial features of feasible configurations that may not be common to all search strategies and discuss how this may impact the practical usefulness of a search strategy.

\subsection{Overall discovered decision space} \label{results-sporeplots}

The four considered search strategies showcase substantial differences in the overall discovered decision space, as shown in Figure \ref{fig:sporeplot}. These differences can be even more marked if considering only those alternatives generated within the main batch of SPORES (see Figure \ref{fig:sporeplot_main_batch}), but are mitigated when parallel batches with technology-explicit secondary objectives are included, as per the default SPORES workflow presented in subsection \ref{methods-workflow}. This evidence reinforces the importance of systematically exploring the decision space from multiple directions, in line with recent work \cite{lombardi_policy_2020,neumann_broad_nodate}.

\begin{figure}[H]
	\centerline{\includegraphics[width=1\linewidth]{figures/sporeplot_1_excl_4panels.pdf}}
	\caption{Generated alternatives in a tri-dimensional space defined by aggregate renewable (Y-axis), transmission (X-axis) and storage (colour coding) capacity deployment compared to maximum deployment potential. Results are provided for all the four considered search strategies. The cost-optimal solution is identified in each panel by a red marker. }
	\label{fig:sporeplot}
\end{figure}

Regarding trade-offs between spatial versus technology diversity, the \textit{integer} and \textit{relative-deployment} strategies tend to produce less-sparse solutions, with many alternatives almost overlapping in the tri-dimensional space that considers total renewables, transmission and storage capacity deployed. Conversely, the \textit{random} and the \textit{evolving-average} strategies seem to produce more sparsity and push the alternatives away from each other more, despite both still ending up with a non-negligible number of overlapping solutions. This means that the \textit{random} and evolving average strategies are more efficient at discovering markedly distinctive options from a technology perspective, whilst the \textit{relative-deployment} and \textit{integer} methods focus more on spatial diversity around fewer distinctive overall technology mixes. In fact, the alternatives overlapping in the tri-dimensional space outlined in Figure \ref{fig:sporeplot} must not be mistaken for identical, and hence redundant, solutions. On the contrary, they are solutions that, albeit similar in terms of the total deployed capacity of the different technology options, are likely radically different in terms of their spatial configuration of technology deployment. Differences across methods do not change qualitatively for the case in which the secondary objectives aimed at minimising or maximising specific technologies are assigned a relatively weaker weight in the objective function (Figure \ref{fig:sporeplot_01}). However, as expected, the number of alternatives located at the extremes of the decision space -- i.e., those focussing on diversifying the spatial configuration around an explicit high-level technology feature, say minimal deployment of bioenergy -- decreases overall. To further investigate the trade-off between technology and spatial diversity, we move on to looking at metrics related to spatial aspects and real-world concerns that play out on a more local scale.

\subsection{Alternatives that match plausible stakeholder interests} \label{results-useful-spores}

One of the most common real-world concerns when deploying new infrastructure for the energy transition is the social acceptability of on-shore wind farms \cite{price_implications_2020}. It is thus helpful to analyse how many of the generated feasible energy system configurations ensure a reduction in the maximum concentration of on-shore wind farms in any single region relative to the total deployed on-shore wind capacity. Such a metric can be considered a proxy for spatial diversity, allowing us to further investigate whether those search strategies that performed less well in terms of technology diversity actually provide richer insights regarding the decision flexibility for moving capacity spatially. If a search strategy produces relatively more alternatives with a high concentration of wind farms, that means it focuses primarily on reducing wind capacity overall or on moving a highly-concentrated on-shore wind hubs elsewhere without spreading capacity out. Figure \ref{fig:boxplots}a shows the distribution of the maximum on-shore wind concentration across all the SPORES generated by each search strategy. As hinted at by the results in the previous subsection, search strategies that are less efficient at providing technologically-distinctive solutions (such as the \textit{integer} and \textit{relative-deployment} ones) are those with lower median values for on-shore wind farm concentration in single regions. In other words, they generate many solutions in which wind capacity is spread out differently across sub-national regions, which is likely of particular importance when providing practical alternatives to real-world acceptability concerns.

Instead, the search strategies with the wider distribution of values for on-shore wind capacity concentration are the \textit{relative-deployment} and the \textit{evolving-average} ones. However, the median value for the \textit{evolving-average} distribution lies much higher than that of the \textit{relative-deployment} one. This suggests that the \textit{evolving-average} method is highly efficient at spanning the full spectrum of wind concentration options, but -- given the finite number of alternatives -- does so at the expense of generating further spatial diversity around the found configurations. In other words, there is a trade-off between using the limited computational power and time to efficiently span all the most radically different options from a high-level perspective versus generating many feasible spatial configurations in the `technology-neighbourhood' of fewer feasible system configurations.

\begin{figure}[H]
	\centering
	\includegraphics[width=1\linewidth]{figures/boxplots_1_excl.pdf}
	\caption{ The top of the Figure shows the data distribution (boxplots) for the maximum concentration of deployed on-shore wind capacity in a single region (across all 97 model locations) compared to total deployed on-shore wind capacity. The boxplots are shown for the four tested weight-assignment methods (panel \textbf{a}) and for a \textit{hybrid} case that combines ex-post the data from the \textit{relative-deployment} and \textit{evolving-average} methods (panel \textbf{b}); see subsection \ref{results-useful-spores} for further details about this \textit{hybrid} case. The bottom of the Figure shows the fraction of generated alternatives that have a concentration of on-shore wind capacity at least 20\% lower than in the cost-optimal case. Results are again shown for the four tested weight-assignment methods (panel \textbf{c}) and for the \textit{hybrid} case (\textbf{d}). }
	\label{fig:boxplots}
\end{figure}

Another way to quantify this trade-off is to count, for each search strategy, how many of the generated alternatives match the plausible stakeholder interest of limiting on-shore wind capacity concentration. For instance, filtering out alternatives in which on-shore wind capacity is at least 20\% less concentrated than in the least-cost case, as done in previous work \cite{lombardi_policy_2020}. The outcomes of such filtering (Figure \ref{fig:boxplots}c) confirm that the \textit{integer} and, primarily, \textit{relative-deployment} search strategies outperform the others from a spatial-diversity perspective. This is in line with our expectations, since the \textit{relative-deployment} weight-assignment method was designed precisely to focus more on spatial diversity (see subsection \ref{methods-spores-weight}). The results of the \textit{random} and, particularly, \textit{evolving-average} approaches further strengthen the finding that, the more a method is efficient at spanning high-level system configurations, the less it is efficient at producing spatially-diverse, non-concentrated system configurations.

Based on this apparent trade-off, combining the benefits and limitations of different methods into a \textit{hybrid} search may be helpful. For instance, the main batch of SPORES in which the focus is on spatial diversity may be generated via the \textit{evolving-average} method, which will make sure to span an even range of technology options. The parallel batches, in which technology diversity is handled explicitly, may instead use a \textit{relative-deployment} method to ensure that configurations are also spatially distinctive. The outcomes of such a \textit{hybrid} method are shown in Figure \ref{fig:boxplots}b,d, resulting in a good compromise in terms of stakeholder-appealing, low-wind-concentration solutions.

The results do not change substantially for a weaker anchoring of the search to the extremes of the decision space, except for the \textit{relative-deployment} search strategy that experiences a reduction in the total amount of alternatives with a low concentration of wind farms (Figure \ref{fig:boxplots_01}). This peculiarity is coherent with our finding so far that the \textit{relative-deployment} method is the one that most efficiently explores the flexibility for moving capacity spatially. The \textit{relative-deployment} search strategy takes advantage of his high spatial focus to generate many spatially-distinctive alternatives around extreme technology features of the decision space within parallel batches of SPORES (see sub-section \ref{methods-spores-parallel}). When the anchoring to extreme technology features becomes weaker, the search departs too quickly from the given extreme technology feature for the \textit{relative-deployment} method to generate many low-concentration alternatives around it. Other weight-assignment methods are less affected by the same phenomenon because they more naturally tend to depart from the extreme technology feature regardless of the strength of the anchoring. Such a worsening of the performance affects, in turn, also the \textit{hybrid} method. Fine-tuning the relative strength of the two objectives in Equation \ref{eq:extra_spores} to the chosen search strategy appears thus essential to maximise the performance.

\subsection{Unique spatial features} \label{results-maps}

We have discussed the concentration of on-shore wind capacity as an example of a plausible criterion for stakeholder discussion and observed a difference across search strategies in the amount of alternatives which limit it. This difference can have very concrete implications. In line with previous work \cite{lombardi_policy_2020,pickering_diversity_2022}, let us assume that stakeholders may be interested in filtering out the decision space based on multiple criteria at once. For instance, minimising the deployment of bioenergy power plants while simultaneously reducing on-shore wind capacity concentration (with the same threshold as defined in subsection \ref{results-useful-spores}). In this case, the decision spaces discovered by the four search strategies would provide very different outcomes.

\begin{figure}
  \vspace*{-2cm}
  \noindent\makebox[\textwidth]{\includegraphics[width=1.2\textwidth]{figures/map_plot_twin.pdf}}

  \caption{SPORES with the lowest concentration of on-shore wind capacity and simultaneous minimisation of bioenergy capacity in the \textit{random} (left) and \textit{relative-deployment} (right) solution spaces. Panels \textbf{a--b} show the deployed capacity of solar (rooftop plus open-field PV) and wind (on-shore plus off-shore) in each of the 97 model locations where the locally-deployed capacity is of at least 10 GW. Panels \textbf{c--d} show the spatial deployment of battery and hydrogen storage (discharge) capacity where the locally-deployed capacity is of at least 1.5 GW. Panels \textbf{e--f} show the capacity of transmission lines connecting each location, including both existing and expanded line capacity. Panels \textbf{g--h} show the system-wide capacity deployed for all considered generation and storage technologies.}
  \label{fig:maps}
\end{figure}

The feasible solutions found by the \textit{integer} and \textit{evolving-average} strategies do not feature any option that satisfies the above criteria at once. Minimising bioenergy appears to be only possible if accepting large, concentrated on-shore wind power hubs in key regions. Conversely, both the \textit{relative-deployment} and \textit{random} search strategies include system configurations that satisfy the chosen criteria. Figure \ref{fig:maps} shows, in particular, the feasible system configurations with the lowest concentration of on-shore wind farms that simultaneously allow the reliance on bioenergy to be completely avoided for both the \textit{random} and the \textit{relative-deployment} solution spaces. Albeit both homogeneous in their wind capacity deployment, the configuration found via \textit{random} search (Figure \ref{fig:maps}a,c,e,g) has about three times higher on-shore wind deployment overall. This results in several regions still becoming large wind power hubs, although few to no regions carry an unfair share of total capacity deployment. Instead, the solution discovered by the \textit{relative-deployment} method (Figure \ref{fig:maps}b,d,f,h) is substantially more balanced in terms of technology mix, with off-shore wind and solar capacities being deployed alongside a spatially-homogeneous deployment of on-shore wind capacity. Substantial hydrogen storage and further grid reinforcements towards the Iberian peninsula support the balancing of this configuration. In other words, in this arbitrary yet plausible example, a search strategy that seemed to not be particularly efficient at spanning the solution space if considered from a high-level technology perspective, ends up being the only one capable of identifying a solution that meets certain stakeholder preferences when looked at from the perspective of spatial infrastructure deployment.

The situation changes when there is less of a push towards searching the extremes of the decision space. In this case, no weight-assignment method can discover a configuration that simultaneously avoids bioenergy and limits the concentration of on-shore wind capacity. This is consistent with our expectations. As anticipated in subsection \ref{methods-spores-secondary_obj} and observed also in subsection \ref{results-useful-spores}, the resulting decision space features fewer solutions in which a given technology -- say, bioenergy -- is fully minimised, and more in which its deployment is only partially reduced. Accordingly, there are no more of those apparently redundant alternatives in which a key part of the technology mix remains the same (i.e., no bioenergy) while capacity is deployed differently from a spatial perspective.

\section{Discussion and conclusion} \label{conclusion}

Our empirical comparison of different mathematical strategies for the generation of near-optimal alternatives within large-scale, high-resolution power system models aimed at investigating how to make the best use of limited computational power. We wanted to assess which search strategies may generate redundant alternatives and which ones may not. Our findings allow us to conclude the following.

First, the boundaries of the overall decision space do not change much with the selected search strategy, provided that the extremes of such a decision space are systematically explored. This corroborates the findings of other recent studies \cite{neumann_broad_nodate,pickering_diversity_2022}.

Second, for an arbitrarily fixed number of generated alternatives (i.e., for a given accepted computational effort), there is a clear trade-off between making use of the computational power to discover alternatives in terms of the high-level technology mix; and using it to generate different spatial configurations of technology deployment in the technology-neighbourhood of fewer high-level technology mixes. The research question should hence guide the choice of the search strategy. If the research focus requires an as-homogeneous-as-possible exploration of the high-level technology mixes, the MGA search can be targeted to such an aim. For instance, relying on the \textit{evolving-average} search strategy proposed in this work or using a brute-force optimisation that targets technology mixes \cite{neumann_broad_nodate}. Yet, such a high-level technology focus is likely -- more so, the more limited the computational power available -- to miss out on most of the stakeholder appealing, spatially-distinctive system configurations around the found technology mixes. Accordingly, whenever research aims at providing alternatives to support real-world decisions, spatial diversity should be at the core of the search strategy. This corroborates our observation in section \ref{intro}, based on the literature, that no single method can capture everything, simply because the set of potentially relevant options is infinite.

Third, as a consequence of the above findings, and particularly when MGA is used to support practical decisions, the computational workflow should foresee iterations with the relevant stakeholders. In such a way, the initial, inherently inexhaustive decision space can be refined based on stakeholder feedback, redirecting the available computational power specifically towards their interests and needs. In this framework, it might be ideal to set up the search in a way that initially compromises between spatial and technology diversity, providing a practically helpful overview of the options. The \textit{hybrid} workflow proposed in subsection \ref{results-useful-spores} might be the most suited for such an initial exploration. Another option, albeit more computationally burdensome, could be to integrate the abovementioned brute-force methods for the homogeneous exploration of technology mixes with the search for a few spatially-distinctive solutions around each found technology mix. Regardless of the chosen method, stakeholders may then indicate themselves which additional technology combinations they would like to investigate that are not initially available, or for which of the existing technology features they would like to see more options to locate capacity spatially. The conceptualisation of a coherent method for creating a human-computer feedback loop of such kind is one of the further developments of our method that we are currently investigating.

Overall, our results corroborate that trying to outline what is possible and what is not possible to do in practice, out of a modelling analysis, is a challenging task. Previous studies have exposed the pitfalls of the common practice of relying on single, cost-optimal results, and have emphasised the potential of MGA to mitigate the provision of misleading insights \cite{decarolis_temoa_2016,berntsen_ensuring_2017,neumann_near-optimal_2021,lombardi_policy_2020}. Our study does confirm that MGA has the potential to provide more meaningful and robust samples of the practically feasible decision space, particularly when carried out from multiple search directions, as done in the most recent literature \cite{lombardi_policy_2020,pickering_diversity_2022,neumann_broad_nodate}. Nonetheless, our results also warn that MGA is not a panacea per se and needs to go along with careful modeller judgement. For state-of-the-art energy system models of large size, even around 200 samples of near-optimal alternatives – let alone the standard provision of a single, optimal solution – may not be enough to robustly assess whether a particular system configuration is not feasible near the economic optimum. Our arbitrary yet highly plausible example of the configuration that minimises bioenergy while limiting on-shore wind capacity concentration is one such case in point. For instance, let us assume we had provided results based on only one of the search strategies tested in this work, say the \textit{integer} one. We might have been tempted to conclude that minimising the use of bioenergy in a fully carbon-neutral European power system is only possible if accepting large, concentrated on-shore wind power hubs. Yet, we have seen that other search strategies lead to a different conclusion. Besides increasing the sample size and search directions as we do in a recent study \cite{pickering_diversity_2022} and adopting more balanced search strategies like the \textit{hybrid} one proposed in this work, a computationally inexpensive solution to improve the robustness of modelling results might be the adoption of ad-hoc MGA sensitivity analyses targeted to specific policy-relevant claims. In the example above, we could have customised the MGA search to explicitly look for a few solutions with minimal bioenergy deployment and constrained concentration of on-shore wind capacity, finding out that those do, in fact, exist. Such a simple MGA-based counterfactual experiment may help corroborate modelling results both in conventional cost-optimisation studies and in cases in which high-performance computing facilities are not available and carrying out a very large MGA analysis is impossible.

\section*{Acknowledgements}
This work was funded by the SEEDS project. The SEEDS project is supported by the CHIST-ERA grant CHIST-ERA-19-CES-004, the Swiss National Science Foundation grant number 195537, the Fundação para a Ciência e Tecnologia (FCT) grant number CHIST-ERA/0005/2019, the Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020, Programación Conjunta Internacional 2020 through the Agencia Estatal de Investigación, and the Estonian Research Council grant number 4-8/20/26. Model runs were performed on the ETH Euler cluster.

\section*{Supplementary Results}

\begin{figure}[H]
	\centerline{\includegraphics[width=1\linewidth]{figures/sporeplot_1_excl_4panels_mainbatch.pdf}}
	\caption{Complements Figure 2 showing only the subset of 50 SPORES originating from the main batch (Equation \ref{eq:gen_spores}). Generated alternatives in a tri-dimensional space defined by aggregate renewable (Y-axis), transmission (X-axis) and storage (colour coding) capacity deployment compared to maximum deployment potential. Results are provided for all the four considered search strategies. The cost-optimal solution is identified in each panel by a red marker. }
	\label{fig:sporeplot_main_batch}
\end{figure}

\begin{figure}[H]
	\centerline{\includegraphics[width=1\linewidth]{figures/sporeplot_01_excl_4panels.pdf}}
	\caption{Complements Figure 2 for the case of a weaker $a$ coefficient in Equation 4. Generated alternatives in a tri-dimensional space defined by aggregate renewable (Y-axis), transmission (X-axis) and storage (colour coding) capacity deployment compared to maximum deployment potential. Results are provided for all the four considered search strategies. The cost-optimal solution is identified in each panel by a red marker. }
	\label{fig:sporeplot_01}
\end{figure}

\begin{figure}[H]
	\centering
	\includegraphics[width=1\linewidth]{figures/boxplots_01_excl.pdf}
	\caption{Complements Figure 3 for the case of a weaker $a$ coefficient in Equation 4. The top of the Figure shows the data distribution (boxplots) for the maximum concentration of deployed on-shore wind capacity in a single region (across all the model 97 locations) compared to total deployed on-shore wind capacity. The boxplots are shown for the four tested weight-assignment methods (panel \textbf{a}) and for a hybrid case that combines ex-post the data from the relative-deployment and evolving-average methods (panel \textbf{b}); see subsection \ref{results-useful-spores} for further details about such a hybrid case. The bottom of the Figure shows the absolute number of generated alternatives that have a concentration of on-shore wind capacity at least 20\% lower than in the cost-optimal case. Results are again showed for the four tested weight-assignment methods (panel \textbf{c}) and for the hybrid case (\textbf{d}). }
	\label{fig:boxplots_01}
\end{figure}

\pagebreak

\section*{Supplementary Methods} \label{supp_methods}

The model we use in this study is based on the open-source Sector-Coupled Euro-Calliope modelling workflow (\url{https://github.com/calliope-project/sector-coupled-euro-calliope}). More precisely, we customise a pre-built Sector-Coupled Euro-Calliope model to focus only on the power sector. The main customisations we apply here are the following:

\begin{itemize}
    \item We do not load any model scenario related to sectors other than power.
    \item We modify the electricity demand timeseries file, originally net of the electricity consumed in other sectors, to include also the electricity that is currently consumed for heat, mobility or industrial energy services.
    \item We eliminate Iceland from the analysis, since we do not model geothermal energy, which accounts for about 30\% of Iceland's electricity production today. This does not have much of an impact on results, due to the small size of the country and its distance from the rest of the continent.
    \item We introduce new, ad-hoc SPORES constraints in the model, which require a specific Calliope environment to be run. We include the requirement and indications about how to meet them in our public release of the code.
\end{itemize}

We make the code openly available on Zenodo (\url{https://doi.org/10.5281/zenodo.6655601}), including brief documentation about how to use it.

In addition to the information provided in sub-section \ref{methods-eurocalliope}, further details about the Euro-Calliope model that we use as a starting point, including technology cost assumptions, are available from the associated publication \cite{pickering_diversity_2022}.

redpajama_set_name: RedPajamaArXiv


I've observed the 4th of July in a lot of places – different states, even different countries – but I've never spent the holiday at the hospital. It is quiet here; a skeleton staff is holding things together while most of their colleagues are off for the day (and night). Even the coffee shop is closed.
I've got my keys (access to 4 hospitals) and my badge (so I can swipe my way into the Emergency Rooms). The hallways are unusually quiet, but in each room there is a pulse. It might be the pulse of a heartbeat, or a pulse of hope…perhaps both. But it is there.
Most years, I celebrate Independence Day with fireworks – a boisterous community observance. But today it seems more appropriate to send out a hug to those who are waiting and watching and wanting something more personal to celebrate.
This entry was posted in CPE, Love by Mary Keenan. Bookmark the permalink.


text:
Turns out there's a whole bunch of them out there.
How to Train Your Dragon 2 (2014)
Quizzes: Cate Blanchett Quiz
https://getyarn.io/yarn-clip/494a96cc-81a3-4cbb-a7cb-314f72a9a4b8
#surprise
#ok
#gasps
#farting
Brooklyn Nine-Nine (2013) - S01E01
And it turns out there were a bunch of references
There's a bunch of bulldozers out there.
Get Out (2017)
and there could be a whole bunch of brothers they got already.
Road Trip (2000)
There's a whole bunch of them. They're like loopholes.
A whole bunch of nothing.
THERE'S A WHOLE WORLD OUT THERE.
see, there's a bunch of idiots out there
Arrested Development (2003) - S04E03 Indian Takers
If I'd told them we were taking a bunch of gays out there
The Blue Lagoon (1980)
There's a bunch of them up there.
The Powerpuff Girls (2002)
See, there's a whole bunch of boxes.
Ray Stevens - Obama Budget Plan
There was a whole bunch of
Lost (2004) - S01E04 Drama
Bury them? There's a whole bunch in there.
Breaking Bad (2008) - S02E05 Drama
All I know is there's a whole world of them out there.
Oh, as it turns out, it's just a whole bunch of nothing.
El Camino Christmas | Official Trailer [HD] | Netflix
It's a bunch of dumb shits out there
Bad News Bears (2005)
- Get out there! - You guys are a bunch of punks.
Fresh Off the Boat (2015) - S03E14 The Gloves Are Off
Somebody ripped out a bunch of seat protectors in there
Rick and Morty (2013) - S01E04
It's just a bunch of 1s and 0s out there.
We've got a bunch of fucking vampires out there,
Kevin Can Wait (2016) - S01E04 Kevin and Donna's Book Club
I got a bunch of hungry ladies out there.
xXx: Return of Xander Cage (2017) Adventure
Turns out there's a new mission.
Well, you know, you shit out a bunch of kids...
but there's a whole cavalcade of demons out there, and...
The Office (2005) - S07E17 Threat Level Midnight
There's a whole crowd of people out there
There's a whole group of guys out there
UnReal (2015) - S02E03
There's a whole world of great content out there.
How to Train Your Dragon 2 Trailer
There's a whole other world of dragons out there.

redpajama_set_name: RedPajamaCommonCrawl


text:
Penn State vs. Indiana Preview
Story posted February 12, 2014 in CommRadio, Sports by Zach Lacher
Penn State is looking to snap a two game losing skid tonight against the Indiana Hoosiers. The Nittany Lions are coming off a tough 60-55 defeat last Sunday at the hands of their Big Ten rival Illinois.
Penn State junior guard DJ Newbill is continuing to impress, averaging a little over 17 PPG and coming off a 19 point, six rebound performance against Illinois. Newbill's teammate Tim Frazier is looking to rebound after a rare subpar game in which he only recorded 11 points and two assists, well below his season averages of 16.2 PPG and 6 APG.
Indiana's 14-9 record doesn't describe the deep talent pool on its roster. The Hoosiers opened the season hot out of the gate, jumping out to a 10-3 start. However, they've slowed down since the start of their Big Ten schedule, going only 4-6 in their last ten games.
Indiana has shown potential despite a couple of disappointing losses to Nebraska and Northwestern. The Hoosiers managed to knock off No.3 Wisconsin in a 75-72 nail-bitter and No.10 Michigan 63-52 behind a game high 27 points from sophomore guard Yogi Ferrell.
Ferrell, who is averaging almost a little over 17 points and 4 assists per game, will look to make an impact when the Lions come to town.
The Indianapolis native will be flanked by freshman phenom Noah Vonleh, who is essentially putting up a double-double a game from the forward position, averaging 11.7 point and 9.5 rebounds per contest.
Coming down the latter half of the season, this has to be treated as a must win for both teams.
Penn State is sitting at 12-12 (3-8 in conference play) and Indiana has a 4-6 mark in the Big Ten. Both teams appear to be on the outside looking in.
This is a position that the Hoosiers aren't used to being in. Indiana sported a 20-3 (8-2 B1G) record through the first 23 games a season ago.
The Lions, despite their record, have played tough in big games this season, and although hustle points don't show up on the score sheet, they have played the better teams close this season.
Penn State was rewarded for its effort during a signature 71-70 overtime road upset over the No. 24 Ohio State Buckeyes that was highlighted by a last second baseline jumper from Newbill that sealed the victory.
It will be interesting to see how both teams come out, but look for the dynamic duos of these teams in Newbill and Frazier for Penn State and Indiana's Ferrell and Vonleh to set the tone early in this contest. The opening tip is scheduled for 7p.m. at Assembly Hall.
Zachary Lacher is a senior majoring in broadcast journalism. To contact him, email zjl5023@psu.edu.
Photo Courtesy: (AP Photo/Centre Daily Times, Abby Drey)
assembly hall , b1g , big ten basketball , cbb , college basketball , comradio , dj newbill , indiana hoosiers , men's basketball , ncaab , ncaam , noah vonleh , patrick chambers , penn state nittany lions , psu , tim frazier , tom crean , yogi ferrell
Zachary Lacher
Senior / Journalism
Currently a Senior Broadcast Journalism Major from Flushing, Queens looking to pursue a career in the field of sports and hope to one day be a writer, reporter, analyst ,commentator or anything that has to to do with the game of baseball or basketball.
Player Spotlight: Indiana G Yogi Ferrell
Column: Lady Lions Peaking at Right Time


text:
Avery Island
Located in Iberia Parish, Avery Island, the largest of five salt domes along the Louisiana coast, is the home of the McIlhenny Company, maker of Tabasco brand products for more than 140 years.
by Shane K. Bernard
Courtesy of Ogden Museum of Southern Art
Avery Island Salt Mines. Ninas, Paul (artist)
Located in Iberia Parish, Avery Island is the largest of five salt domes along the Louisiana coast. It is the home of the interrelated Avery and McIlhenny families; Jungle Gardens, a 170-acre semitropical estate open to the public since 1935; and the McIlhenny Company, maker of Tabasco brand products for more than 140 years.
Geography and Industry
A geologic oddity, Avery Island rises dramatically from low, flat wetlands to a height of 163 feet above mean sea level—making it the highest point on the entire Gulf Coast. The island is approximately 2,200 acres in area and about three miles in diameter at its widest point. Avery Island is not an island in the traditional sense. Located a few miles inland from the nearest body of open water, it appears insular because of its conspicuous height and encirclement by wetlands. These wetlands include a coastal salt marsh, a cypress swamp, and several bayous, including Stumpy Bayou, Saline Bayou, Bayou Leleu, and, most notably, Bayou Petite Anse.
The island's primary industries are oil production (since 1942); salt mining (since 1862); the manufacture of Tabasco brand products; and tourism, driven by public access to the Tabasco factory and Jungle Gardens. At various times during the early twentieth century the island produced sand, gravel, and lumber, as well as canned shrimp, oysters, fruit, and vegetables. Fur trapping in the nearby salt marsh once played a vital role in the local economy. Sugarcane production remained a major industry on the island until around 1925, when the Averys no longer found the crop commercially viable.
Avery Island formed over millions of years as a buoyant subterranean plume of solid rock salt pushed up the earth's surface. Fossil records indicate that about ten thousand years ago the island's briny spring water attracted a variety of now-extinct animal species. These "megafauna" included mastodons, dire wolves, and giant sloths, among others. Although humans may have first visited the island at that time—attracted not only by its salty springs, but by the ample game—radiocarbon dating suggests that Native Americans lived on the island by 2500 BC. Other "pre-tribal" Native Americans subsequently came to the island to extract salt from its springs through boiling. Thousands of pottery shards collected around the island reflect the scope of this industry.
History and Ownership
Europeans encountered Avery Island as early as 1779, when French explorers employed by Louisiana's Spanish administrators described a "so-called isle" near Bayou Petite Anse (meaning "Little Cove" in Louisiana French). The island itself soon became known as Île Petite Anse or Petite Anse Island, though for a short time around 1800 it bore the names Isla Cuarin and Côte de Coiron. The latter two names alluded to the island's earliest known owner, a Dr. Cuarin or Coiron, who claimed the island in the late 1700s. By the early 1800s, however, several pioneer settlers owned parts of the island. These settlers included Elizabeth Hayes, Jacques Fontenette, Alexandre Devince Bienvenu, Boyd Smith, and Jesse McCall.
In 1818, New Jersey native John Craig Marsh purchased a share of the island and began to plant sugarcane on it. In the early 1850s, Marsh sold his plantation to his son, George Marsh, and to his two sons-in-law, Ashbel Burnham Henshaw and Baton Rouge jurist D. D. Avery, the latter of whom soon came into possession of the other's shares.
During the Civil War (1861–1865), Confederate troops under General Richard Taylor defended the island's mines, which provided salt to several states in the lower South. In November 1862, the Union unsuccessfully attacked the island by water using two gunboats and a transport ship. Federal troops finally captured the island by land in April 1863, destroying some mining equipment and outbuildings. Except for two Avery sons who served as Confederate army officers, the Avery and McIlhenny families fled to Texas for the remainder of the conflict.
After the war the Averys and McIlhennys returned to the island, and in 1868, D. D. Avery bought out the last of the early landholders—making Avery the island's sole owner. On his death in 1879, ownership passed to Avery's five children: state militia leader Dudley Avery, Louisiana legislator John Marsh Avery, Sarah Marsh Avery Leeds (wife of Paul B. Leeds, whose family ran the Leeds Foundry in New Orleans), Margaret Henshaw Avery Johnston (whose husband, soldier and poet William Preston Johnston, served as president of Louisiana State University and first president of Tulane University), and Mary Eliza Avery McIlhenny (wife of Tabasco sauce inventor E. McIlhenny).
In 1903, the surviving Avery siblings organized their plantation into a modern corporation, called the Avery Planting and Improvement Company. In 1924 this entity became the Petit Anse [sic] Company, which in turn became Avery Island, Inc., in 1948. Enthusiastic conservationists, the McIlhenny branch of the Avery family adopted the motto "Man and Environment in Balance" in 1971. Their environmental efforts, however, actually extend back to around 1895, when E. A. McIlhenny founded a private wildfowl refuge on the island. This refuge still exists in Jungle Gardens and annually attracts tens of thousands of birds (primarily egrets).
Avery Island, Inc., continues to control the island's surface and mineral rights. Present-day shareholders are required by corporate charter to be direct descendants of D. D. Avery, the man in whose honor Petite Anse Island was renamed in the late nineteenth century.
Bernard, Shane K. Tabasco: An Illustrated History. Avery Island, LA: McIlhenny Company, 2007.
Meek, A. J. Red Pepper Paradise: Avery Island, Louisiana. New Orleans, LA: Audubon Park Press, 1986.
Osborn, Lisa B., Shane K. Bernard, and Scott Carroll. The History of Jungle Gardens Avery Island, LA: Jungle Gardens, Inc., 2010.
Entry Last Updated June 12, 2018
Category Foodways, History
Regions Southwest Louisiana (Acadiana), Iberia
Time Periods Civil War Period, Contemporary Period, Late-20th Century, Long Era
Index letter A
Pokey McIlhenny
Teddy Roosevelt's Bear Hunt
Avery Island Salt Mines
Pepper Fields of Avery Island
Mary Eliza Avery McIlhenny
This portrait is of Edmund McIlhenny, the inventor of Tabasco sauce.
The Tabasco factory on Avery Island
Tabasco Sauce Bottles
Avery Island from a distance
A map of Avery Island
The Bird City wildfowl refuge


Scott Morrison and the Liberals and Nationals cut schools, hospitals, TAFE and pensions all to pay for their $80 billion handout for banks and multinationals.
✘ Cuts to TAFE and apprenticeships.
We need your help to fight Scott Morrison's cuts. Can you sign our petition and stand against Scott Morrison's cuts?
When it comes to what matters to middle class and working Australians, Scott Morrison and the Liberals and Nationals are only for the top end of town.
As Treasurer, Scott Morrison spent three years trying to give the banks a $17 billion tax cut.
He cut $14 billion from schools.
He tried to give an $80 billion taxpayer-funded handout to multinationals and big business.
He cut $700 million from local hospitals, and he supported cuts to penalty rates.
He voted to raise the pension age to 70, and to cut the pension.
As Treasurer Scott Morrison cut funding from health while trying to give a $80 billion tax handout to big business, including $17b to the big banks.
✘ Approved private health insurance premium increases costing families more than $1,000 extra each year for coverage.
Cutting funding from public schools means more pressure on class sizes and less individual attention for students.
You can't have a plan for economic growth without a plan to invest in schools and education.
Scott Morrison and the Liberals/Nationals have cut $3 billion from TAFE and apprenticeships.
Under the Liberals/Nationals Australia has lost 150,000 apprenticeship places nationwide.
If you are serious about jobs you don't cut funding from TAFE.
As Treasurer Scott Morrison cut $2.2 billion from universities, pushing up fees, and increasing lecture and tutorial sizes.
This is putting university study out of reach for many kids from middle and working income families.
We can't have skilled jobs and a strong economy if Scott Morrison is cutting education and university funding.
As Treasurer Scott Morrison voted eight times for cuts to penalty rates which leave hundreds of thousands of workers worse off.
These cuts affect workers in retail, hospitality, fast food, pharmacies and restaurants, with take home pay being cut by up to $3,200 this year.
We need a wage rise for working Australians, not tax handouts for big banks and multinationals.
✘ Cut concessions that help pensioners with costs including rates and registration.


text:
Our Eyes Were Opened
Beth Lindsay Templeton
Simulations & Tours
Uncharted Journey
Books by Beth Lindsay Templeton
Refrigerator Prayers for Ordinary People
Home > books > Refrigerator Prayers for Ordinary People
Refrigerator Prayers for Ordinary People provides daily prayers for those times when you need help getting started with your own prayer for the day or for when you just can't think of anything to say to God. You may find various prayers to be profound, challenging, honest, and sometimes even humorous. The intent of each prayer is to help you on your journey with our Holy God.
Beth Templeton has passion for people who live in poverty, the heart of a servant, the commonsense of a wise woman, and the joy of a faith-filled life. People who experience her, whether rich or poor, realize changes in how they think about life and how they compassionately live together in community.
Richard H. Riley Former Governor of South Carolina and former U.S. Secretary of Education
As a community activist, author, minister, consultant and teacher, Beth Templeton hosts a variety of workshops to help remove our "blinders" when addressing persistent poverty in our society.
Our Eyes Were Opened helps people understand about poverty so they can reach out with wisdom and compassion.
Conversing With God = Prayer
Christmas Questions
What Does Gentrifying Mean?
Connect with Beth
Address: PO Box 73 Greenville SC 29602
Website: http://oureyeswereopened.org
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International Food Policy Research Institute Podcasts
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IFPRI Podcast
The International Food Policy Research Institute (IFPRI) provides research-based policy solutions to sustainably reduce poverty and end hunger and malnutrition in developing countries. Established in 1975, IFPRI currently has more than 600 employees working in over 50 countries. It is a research center of CGIAR, a worldwide partnership engaged in agricultural research for development.
Bologna Institute for Policy Research
The Bologna Institute for Policy Research (BIPR) is the research division of the Johns Hopkins University School of Advanced International Studies (SAIS) in Europe. Its purpose is to promote problem-centered, interdisciplinary research in international policy by drawing upon the global network of SAIS faculty, students and scholars. BIPR SeminarCasts are made available to the public with the goal of providing a pivotal forum for thought and debate in international public policy.
IFPRI Podcasts
The International Food Policy Research Institute (IFPRI) seeks sustainable solutions for ending hunger and poverty.
Local vs Global? The role of trade in building food system resilience 1:35:10
POLICY SEMINARLocal vs Global? The role of trade in building food system resilienceJAN 13, 2021 - 09:30 AM TO 11:00 AM ESTAs the COVID-19 pandemic continues to wreak havoc on the global economy and put pressure on food supply chains, debate has resurged about the role of trade in protecting food security. Though food supply chains have proved fairl…
CGIAR COVID-19 Hub: Supporting National Responses to a Global Pandemic 1:30:45
Virtual Event - CGIAR COVID-19 Hub: Supporting National Responses to a Global PandemicCo-Organized by IFPRI and CGIAR COVID-19 HubJAN 12, 2021 - 09:30 AM TO 11:00 AM ESTAs the global leader in agricultural research, CGIAR took action in the earliest days of the pandemic to counter the potentially devastating impact on food security and nutrition wo…
How Can WTO Members Support Recovery & Resilience in South Asia? 1:27:16
The coronavirus outbreak has affected trade in food and farm goods, imperiling efforts to reduce hunger and malnutrition and adding to pressure already on the farm sector from climate change. The pandemic-induced postponement of the WTO's next ministerial conference and ongoing tensions among major economies have affected both the substance and pro…
Legalization of Migrant Workers under Covid 19: A Tale of Two Countries 1:29:04
Giulia Crescini, Association for Juridical Studies on Immigration (ASGI), Italy, America Hernandez, Kids in Need of Defense (KIND), US and Alexandra Malangone, Center for Constitutional Studies and Democratic Development (CCSDD), Italy, discussed "Legalization of Migrant Workers under Covid 19: A Tale of Two Countries" in a SAIS Europe webinar on D…
PIM Webinar: COVID-19 Risk and Food Value Chain (12/11/2020) 1:05:26
This webinar organized by the CGIAR Research Program on Policies, Institutions, and Markets (PIM) and Food Security Portal on December 11, 2020 shares results of recent studies in Ethiopia, Nigeria, and India on how food value chains are responding to COVID-19. Kalle Hirvonen (IFPRI) presents findings from a study on food consumption and food secur…
The African Continental Free Trade Area: How will economic distribution change? 1:16:24
Virtual Event - The African Continental Free Trade Area: How will economic distribution change?DEC 15, 2020 - 09:30 AM TO 10:45 AM ESTIn early 2021, African countries will begin implementing the much-anticipated African Continental Free Trade Area (AfCFTA). The Africa-wide elimination of tariffs will encourage regional integration to a degree never…
Closing Remarks and Way Forward 4:00
Closing Remarks and Way ForwardDecember 08, 2020Speaker:Dr. Clemens Breisinger, MENA and Egypt Program Leader and Senior Research Fellow, IFPRILINKS:Symposium Policy Notes (in Englisha and Arabic): https://ebrary.ifpri.org/digital/collection/p15738coll2/search/searchterm/Symposium%20policy%20note/field/title/mode/all/conn/and/order/title/ad/ascOthe…
Session 2: Digitalization of agricultural services and policy analysis 11:52
Session 2: Digitalization of agricultural services and policy analysisDecember 08, 2020Brief overview of key recommendations from policy note:Ms. Yumna Kassim, IFPRIPresentation of Monitoring Agricultural Projects in Egypt (MAP Egypt)Ms. Hagar ElDidi, IFPRICommentator:Dr. Ismail El Faramawy, Italian Agency for Development Cooperation and Developmen…
Session 1: Promoting food system transformation and resilience post-COVID 19 28:28
Session 1: Promoting food system transformation and resilience post-COVID 19December 08, 2020Brief overview of key recommendations from policy notesDr. Mariam Raouf, IFPRI and Mr. Hosam Ibrahim, IFPRICommentators:Dr. Mouchera Karara, the Ministry of Planning and Economic DevelopmentDr. Abla Abdel Latif, Egyptian Center for Economic StudiesBrief ove…
Opening Remarks: Impact of policy research and lessons for post-COVID development 55:53
Opening Remarks: Impact of policy research and lessons for post-COVID developmentDecember 08, 2020Speakers:H.E. Dr. Hala El-Said, Minister of Planning and Economic DevelopmentH.E. Dr. Nivine Elkabbag, Minister of Social SolidarityDr. Saad Mousa, General Supervisor of Foreign Agricultural Relations, Ministry of Agriculture and Land ReclamationMs. Le…
Arafat and Abbas: Portraits of Leadership in a State Postponed 48:26
Menachem Klein, Bar-Ilan University, Israel, discusses "Arafat and Abbas: Portraits of Leadership in a State Postponed" in a SAIS Europe webinar on December 10, 2020.By Menachem Klein
Martin J. Forman Memorial Lecture: Seizing Opportunity from the Jaws of Crisis 1:29:30
Global nutrition priorities, financing, and governance have evolved significantly over the last two decades. The translation of scientific evidence and cross-sector program data and experience has played a leading role in this evolution. But evidence alone is insufficient to stimulate broad-based action and enduring nutrition change.At the 30th ann…
Sweden's Feminist Foreign Policy 1:09:19
Ambassador Veronika Wand-Danielsson, Americas Department, Swedish Foreign Ministry, discussed "Sweden's Feminist Foreign Policy" in a SAIS Europe webinar of December 7, 2020.By Ambassador Veronika Wand-Danielsson
PIM Webinar: Writing and Responding to Referee Reports (12/07/2020) 1:28:36
Publishing Agricultural Development Research in Social Science Journals: Writing and Responding to Referee ReportsPIM WebinarDecember 7, 2020This PIM Webinar Co-organized by the CGIAR Research Program on Policies, Institutions, and Markets (PIM), the CGIAR Standing Panel on Impact Assessment (SPIA), and African Women in Agricultural Research and De…
How Can WTO Members Support Recovery & Resilience in Africa (English panel)? 1:06:31
Virtual Event - Food and Agricultural Trade in the New Policy Environment: How Can WTO Members Support Recovery and Resilience in Africa (English panel)?Co-Organized by IFPRI, Akademiya2063, and International Institute for Sustainable Development (IISD)DEC 8, 2020 - 09:30 AM TO 10:30 AM ESTThe coronavirus outbreak has affected trade in food and far…
Virtual Event - Informal African trade: The hidden world of food flows 1:13:59
Intra-African trade—especially agricultural trade—is much larger than official statistics suggest. In West Africa, an estimated 30 percent of staple foods cross borders informally, and informal trade across some African borders—particularly for perishable foods like fruits and vegetables—far exceeds formal trade. Recent initiatives have made great …
Virtual Event - Food Policy Research and Capacity Development in Eurasia 1:32:01
Virtual Event - Food Policy Research and Capacity Development in EurasiaCo-Organized by the Eurasian Center for Food Security (ECFS), World Bank Group, and IFPRIStrengthening policy research and analytical capacity in Eurasia is critical for the region's agricultural transformation and for development of inclusive food systems. A recent program sup…
The New Nutrition Reality: Time to Recognize and Tackle the Double Burden of Malnutrition! 1:48:18
The world is facing a new nutrition reality in which persistent undernutrition and escalating overweight and obesity coexist within countries, communities, households, and individuals. This double burden of malnutrition brings a set of new challenges for policy and programming.This seminar presented new global evidence on the magnitude of the doubl…
Publishing Agricultural Development Research in Social Science Journals: Advice from Journal Editors 1:33:28
This webinar, the 2nd in the series "Publishing Agricultural Development Research in Social Science Journals", offers a panel discussion amongst editors or associate editors of leading journals, addressing what they look for in submissions, how to avoid "desk rejections", how to handle reviews, proofing, and publicizing articles. More info about th…
The Legitimacy of International Organizations 52:51
Jonas Tallberg, Stockholm University, Sweden, discussed "The Legitimacy of International Organizations" in a SAIS Europe webinar on November 30, 2020.By Jonas Tallberg
A Career in European Foreign Policy 31:58
Helen Campbell, European External Action Service, Belgium, discusses "A Career in European Foreign Policy" in a SAIS Europe webinar on November 26, 2020.By Helen Campbell
Virtual Event - Information Needs for Food Crisis Risk Early Warning 1:39:22
Virtual Event - Information Needs for Food Crisis Risk Early Warning: The Role of the Food Security PortalCo-Organized by IFPRI and the Food Security Portal (FSP)NOV 24, 2020 - 09:30 AM TO 11:00 AM ESTPersistent drivers of food insecurity and acute hunger—conflict, climate shocks, and environmental shocks—have been compounded in 2020 by the COVID-1…
PIM Webinar: Migration and gender dynamics in irrigation governance in Nepal (Nov. 25, 2020) 1:28:28
This PIM webinar was recorded November 25, 2020 and convened by Farmer Managed Irrigation System Promotion Trust (FMIST - https://fmistnepal.wordpress.com/), Nepal; International Food Policy Research Institute (IFPRI-http://www.ifpri.org/); International Water Management Institute (IWMI- https://www.iwmi.cgiar.org/); CGIAR Research Programs on Poli…
Business and Human Rights in the Agri-food Industry: The Case of Migrant Workers 1:31:07
Panel discussion on "Business and Human Rights in the Agri-food Industry: The Case of Migrant Workers" in a SAIS Europe webinar on November 23, 2020. Featuring: Nina Luzzatto Gardner - Chair, Johns Hopkins University SAIS, US, Sara Pennicino, Johns Hopkins University SAIS Europe; University of Padua, Italy, Laura Safer Espinoza, Fair Food Standards…
IFPRI Policy Seminar in Spanish: Food and Agricultural Trade in the New Policy Environment 1:00:07
Virtual Event - Food and Agricultural Trade in the New Policy Environment: How Can WTO Members Support Economic Recovery and Resilience in Latin America and the Caribbean?Co-Organized by IFPRI, Inter-American Institute for Cooperation on Agriculture (IICA), and International Institute for Sustainable Development (IISD)The coronavirus outbreak has a…
PIM Webinar: The Journal Publication Landscape (Nov. 23, 2020) 1:32:00
This PIM Webinar co-organized by the CGIAR Research Program on Policies, Institutions, and Markets (PIM), the CGIAR Standing Panel on Impact Assessment (SPIA), and African Women in Agricultural Research and Development (AWARD), was recorded on November 23, 2020, the 1st in the series "Publishing Agricultural Development Research in Social Science J…
Fake It Till You Make It: The Future of Democracy in Central Eastern Europe 40:20
Veronica Anghel, European University Institute; Johns Hopkins University, SAIS Europe, Italy, discussed "Fake It Till You Make It: The Future of Democracy in Central Eastern Europe" in as SAIS Europe webinar on November 19, 2020.By Veronica Anghel
BOOK PRESENTATION - European Integration: A Political History 44:03
Mark Gilbert, Professor of History and International Studies at the Johns Hopkins University SAIs Europe, presented his latest book in a webinar on November 16, 2020.By Mark Gilbert (Author) - Vincent Della Sala (Discussant)
Évènement virtuel - Commerce alimentaire et agricole dans le nouvel environnement politique 1:07:53
Le commerce des denrées alimentaires et des produits agricoles a été affecté par la pandémie du COVID-19, mettant en péril les efforts visant à faire progresser la lutte contre la faim et la malnutrition conformément au cadre défini par les Objectifs de développement durable (ODD). En même temps, le secteur agricole subit une pression croissante po…
Challenges, Crises and Emergency Government: The Existential Crisis of the European Union Revisited 50:30
Agustín José Menéndez, Universidad Autónoma de Madrid, Spain, discusses "Challenges, Crises and Emergency Government: The Existential Crisis of the European Union Revisited" on November 12 in a SAIS Europe webinar.By Agustín José Menéndez
The 2020 U.S. Presidential Elections - A Preliminary Assessment 48:06
Johns Hopkins SAIS Europe hosted a virtual panel discussion on "The 2020 U.S. Presidential Elections - A Preliminary Assessment" on November 9, 2020.By Catherine Fieschi - Justin O. Frosini - Erik Jones - Joe Renouard
The Great Lockdown: Dissecting the Economic Effects 32:46
Francesco Grigoli, International Monetary Fund, US, discusses "The Great Lockdown: Dissecting the Economic Effects" in a webinar hosted by Johns Hopkins SAIS Europe on November 2, 2020.By Francesco Grigoli
Access to Credit under the Microscope: Financial Inclusion from Microfinance to Fintech 45:56
Andrea Presbitero, Vera and Stefano Zamagni Professor of Economics, Johns Hopkins University SAIS Europe, Italy, discusses "Access to Credit under the Microscope: Financial Inclusion from Microfinance to Fintech" on October 22, in a SAIS Europe webinar.By Andrea Presbitero
Virtual Event - The political economy of COVID-19: Impacts on agriculture and food policies 1:29:19
The economic impacts of the COVID-19 pandemic and resulting pressures on public finances and donor resources are necessitating stark trade-offs among different policy priorities within the agriculture sector and across the food system. In what ways has the pandemic altered the balance of power among urban and rural populations, the state and privat…
Virtual Event - The African Agriculture Trade Monitor 2020 1:17:38
Against the background of worldwide trade disruptions due to COVID-19, IFPRI and AKADEMIYA2063 are launching the third annual Africa Agriculture Trade Monitor (AATM), the leading comprehensive report on African agricultural trade flows and policies. The report's authors will discuss emerging trends in African countries' trade flows and compositions…
Democracy, Justice, Leadership, and Gender: Global Politics and Public Service from the Cold War to the 21st Century. A Conversation with the Rt. Hon Kim Campbell, Canada's 19th Prime Minister 52:21
RH Kim Campbell, Former Prime Minister of Canada, discusses "Democracy, Justice, Leadership, and Gender: Global Politics and Public Service from the Cold War to the 21st Century" at SAIS Europe on October 15, 2020.By RH Kim Campbell
Virtual Event - COVID-19 and Developing Country Responses 1:03:10
This is a recording of an IFPRI hosted a side event at the World Food Prize 2020 Borlaug Dialogue.The session addresses COVID-19 and Developing-Country Responses, presenting country and regional perspectives including from China, India, Africa south of the Sahara, and a global perspective.Speakers offered brief perspectives followed by an engaging …
Threats and Alliances in the Middle East 31:13
May Darwich, University of Birmingham, UK, discussed "May Darwich, University of Birmingham, UK" on October 8, 2020.By May Darwich
Virtual Event: Invisible China: How the Urban-Rural Divide Threatens China's Rise 1:01:37
China has quickly transformed itself from a place of stark poverty into a modern, urban, technologically savvy economic powerhouse.Authors Scott Rozelle and Natalie Hell draw on extensive surveys on the ground in China and reveal that while China may be the second-largest economy in the world, its labor force has one of the lowest levels of educati…
Building Trust in Uncertain Times: Challenges for Central Bank Communication 1:00:31
Gabriel Glöckler, European Central Bank, Germany, discussed "Building Trust in Uncertain Times: Challenges for Central Bank Communication" on October 5, 2020.By Gabriel Glöckler
China's Western Horizon 48:52
Daniel S. Markey, Johns Hopkins University SAIS, US, discussed "China's Western Horizon" on October 1, 2020.By Daniel S. Markey
Reimagining New Zealand's Role in the World - Book Launch of "Beyond these Shores: Aotearoa and the World" edited by Nina Hall 50:25
Johns Hopkins University SAIS Europe hosted the virtual launch of the book edited by SAIS Europe Professor Nina Hall "Reimagining New Zealand's Role in the World". The book co-authors participated in the discussion: Fairlie Chappuis - Max Harris - Tina Ngata - Tulia Thompson.By Fairlie Chappuis - Nina Hall - Max Harris - Tina Ngata - Tulia Thompson
The New World Disorder: The Power Struggle between China, the US and Europe 48:35
Alexander Stubb, European University Institute, Italy; Former Prime Minister of Finland, delivers a seminar on "The New World Disorder: The Power Struggle between China, the US and Europe" at SAIS Europe.By Alexander Stubb
Virtual Book Launch - Ethiopia's agri-food system 1:32:03
Ethiopia has experienced impressive agricultural growth and poverty reduction, stemming in part from substantial public investments in agriculture. Yet, the agriculture sector now faces increasing land and water constraints along with other challenges to growth. Growth in the agriculture sector remains essential to continued poverty reduction in Et…
PIM Webinar: Improving research engagement to support policy and institutional change 59:53
Too often, research aiming to inform public policies or strengthen institutions for effective policy implementation remains disconnected from the real political economy of policy and institutional reform. This webinar introduces a new rubric to assess opportunities for research partnerships that navigate this complex terrain of power and leverage s…
Italy: No Country for Trivial Elections 56:07
Johns Hopkins University SAIS Europe hosted a panel discussion on "Italy: No Country for Trivial Elections", featuring faculty both from SAIS Europe and from University of Bologna.By Donatella Campus - Justin Frosini - Erik Jones - Gianfranco Pasquino
Virtual Event - COVID-19, global markets and African agricultural trade 1:33:21
The economic shocks of COVID-19 on global markets have left the African continent, along with its producers and consumers, distinctly vulnerable. This seminar will examine how changes in global food trade since the pandemic have impacted African food systems, regional supply chains and food access. Presenters discussed the potential long-term risks…
Book Launch: Agricultural Extension: Global Status and Performance in Selected Countries 1:35:11
Virtual Book LaunchAgricultural Extension: Global Status and Performance in Selected CountriesSEPT 10, 2020 - 09:30 AM TO 11:00 AM EDTThis book provides a global overview of agricultural extension and advisory services, assesses and compares extension systems at the national and regional levels, examines the performance of extension approaches in a…
COVID-19: The role of the agriculture-ecosystem health interface 1:31:55
This policy seminar was recorded Aug. 18,. 2020.Efforts to improve food security and nutrition have contributed to dramatic declines in forest and other natural ecosystems and rapid increases in contact rates between human and wild and domestic animals. Since 1940, agricultural drivers were associated with more than one quarter of all infectious di…
COVID-19's Short-term Impacts on Economies, Food Systems and Poverty in African and Asian Countries 1:16:10
Virtual Event - COVID-19's Short-term Impacts on Economies, Food Systems and Poverty in African and Asian Countries: Economywide Estimates from Economywide ModelsCo-Organized by IFPRI and the CGIAR Research Program on Policies, Institutions, and Markets (PIM)This seminar was recorded on August 11, 2020.Developing countries responded to COVID-19 by …