Spatial economy что это

Пространственная экономика

В статье представлен общий обзор понятий пространственной экономики, который охватывает теорию местоположения, пространственной конкуренции, региональной и городской экономики. После краткого обзора основных теоретических терминов, рассмотрена фундаментальная роль пространственной экономики и ее роль в развитии конкурентных отношений между предприятиями. Также обсуждаются основные проблемы, с которыми сталкиваются теоретические и эмпирические исследования в области пространственной экономики, после чего следует более широкое обсуждение взаимосвязи между этой областью исследований и другими областями экономики и других дисциплин.

Spatial economy

This article presents a general overview of spatial economy, which covers the theory of location, spatial competition, regional and urban economics. After a brief overview of the basic theoretical traditions, the fundamental role of non-convexities in the economy and imperfect competition are mentioned. The main problems of the theoretical and empirical research they are, followed by a broader discussion of the correlation between this area of research and other economic subfields and other disciplines.

Пространственная экономика [1] связана с распределением (дефицитных) ресурсов [2–4] над пространством и местонахождением экономической деятельности. В зависимости от трактовки определения, область пространственной экономики может быть либо чрезвычайно широкой, либо довольно узкой. С одной стороны, экономическая деятельность должна происходить на каком-то пространстве. С другой стороны, анализ сосредоточен, главным образом, на одном экономическом вопросе, а именно — выборе местоположения.

На практике мы можем поставить ряд вопросов, для которых имеет важность пространственное расположение. Например, почему существуют города? Почему некоторые регионы процветают, а другие нет? Почему происходит выделение и дискриминация групп населения? Почему фирмы из одного и того же отраслевого кластера развиваются по‑разному?

Это внутренне «пространственные» вопросы, то есть вопросы, в которых пространственное положение играет доминирующую роль. Например, было бы трудно говорить о существовании и росте городов без какого‑либо явного рассмотрения пространства.

В то же время есть вторая группа проблем, касающихся, в частности, анализа технологических побочных эффектов, детерминант торговли потоков или даже функционирования социальных сетей. Все эти проблемы имеют пространственное измерение, но его значение еще предстоит определить.

Иными словами, это «оспариваемые» проблемы между пространственной экономикой и другими экономиками.

Наконец, существует чрезвычайно широкий круг экономических вопросов, для которых пространственное измерение, вероятно, будет менее важным.

Например, каковы движущие силы инвестиции? Насколько важны затраты на увольнение для объяснения безработицы? Чтобы ответить на эти вопросы, основная роль пространства заключается в предоставлении, возможно, основным источником вариаций эмпирических исследований. Если, например, разные регионы страны имеют разные системы образования с разными возрастными ограничениями для обязательного школьного образования, эти вариации могут быть использованы для получения значимых оценок отдачи от образования.

Источник

Spatial economy что это

Вышел в свет юбилейный, 60-й, Номер журнала «Пространственная экономика», учрежденный Институтом экономических исследований ДВО РАН совместно с Дальневосточным отделением РАН.

Журнал задумывался как печатный орган сообщества российских и зарубежных ученых, работающих в области начавшего оформляться в начале 2000-х годов нового научного направления – пространственной экономики. Это направление возникло не на пустом месте, вобрав в себя теоретические и экспериментальные достижения многих поколений ученых в области размещения и территориальной организации экономической деятельности, региональной экономической политики, закономерностей и проблем функционирования экономических агентов в территориальных системах, пространственной макроэкономики и эконометрики, экономического районирования, системной аналитики и прогнозирования развития региональных хозяйственных комплексов, новой экономической географии и др.

За 15 лет, в течение которых выходит журнал, в нем опубликовано почти 800 научных статей по всем направлениям пространственных экономических и социальных исследований. За эти годы почти 400 авторов из 100 научно-исследовательских, образовательных и иных организаций опубликовали в журнале результаты своих исследований. Журнал предоставляет свои страницы как ведущим российским и зарубежным ученым по проблемам пространственного социально-экономического развития, так и начинающим авторам, многие из которых творчески росли и укрепляли свою научную репутацию вместе с журналом.

Статьи, опубликованные в журнале, цитируются в более, чем 800 научных журналах в России и за рубежом. Только за последние 10 лет импакт-фактор журнала увеличился в 4 раза, достигнув значения 1,93, а в рейтинге Российского индекса научного цитирования журнал поднялся за эти 10 лет с 465 до 34 места, заняв позицию в первой пятерке научно-экономических российских журналов.

Огромную роль в успехах журнала играла и играет самоотверженная работа большого коллектива редакционной коллегии и редакционного совета, в состав которых входят наиболее авторитетные и творчески активные ученые из ведущих российских и зарубежных исследовательских центров, и особенно коллектива редакции журнала. Нельзя с благодарностью не вспомнить неоценимую организационную и финансовую помощь, которую в самые трудные периоды его становления и до сих пор оказывали и оказывают журналу сотрудники редакционно-издательского отдела Дальневосточного отделения РАН, а также наши спонсоры.

Конечно, никакой научный журнал не может состояться без поддержки и творческого энтузиазма авторов. И особо следует отметить беспрецедентный вклад рецензентов, работа которых не видна читателям, но является краеугольным камнем научной репутации самого журнала, оказывая при этом неоценимую помощь не только редакции и редколлегии, но прежде всего самим авторам журнала.

Искренне поздравляю всех наших авторов, рецензентов, членов редакционной коллегии и редакционного совета, сотрудников редакции журнала, весь коллектив Института экономических исследований ДВО РАН, который все эти годы самоотверженно поддерживал журнал.

П.А. Минакир, академик РАН,

главный редактор журнала «Пространственная экономика»

Источник

ПРОСТРАНСТВЕННЫЕ ИССЛЕДОВАНИЯ В РАБОТАХ РОССИЙСКИХ РЕГИОНАЛИСТОВ: НАРРАТИВНЫЙ ОБЗОР

УДК 332.1

Аннотация

Цель настоящей статьи — упорядочить сравнительно новую (за последний десятилетний период) информацию в области региональных и пространственных исследований для лучшего понимания предметного поля регионалистики. Статья выполнена в форме обзора как одного из перспективных видов научной публикации и в целом отвечает международным критериям и традициям их составления.

С учетом первоначального этапа исследования социально-экономического пространства среди обширной типологии обзоров, применяемых в международной практике, выбран наиболее часто встречающийся вид — нарративный обзор, одной из характерных черт которого является субъективный отбор информации.

Предпочтение при отборе статей отдавалось работам известных регионалистов; при этом в обзор попали не только публикации, «прямо» отражающие тенденции развития социально-экономического пространства, но и исследования, выполненные по сопряженным тематикам и дисциплинам.

Структуризация статей проведена по различным основаниям. Главные из них — группировка публикаций: 1) в разрезе научных школ и персоналий; 2) в зависимости от направленности публикаций, раскрывающих те или иные тенденции развития социально-экономического пространства («дифференциация — нивелирование» различий, «сжатие — расширение», «интеграция — фрагментация»).

Раскрыты трудности и проблемы формирования обзоров региональных и пространственных исследований, показаны перспективные направления анализа информации по пространственной тематике, связанные с подготовкой систематического обзора.

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доктор социологических наук, профессор кафедры экономики и эконометрики ; заведующий лабораторией социальноэкономических исследований

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Источник

Spatial Economics

Related terms:

Scaling: Multidimensional

1.2 Geography/Economics

GIS Applications for Socio-Economics and Humanity

Abstract

The main social problem associated with the Internal Armed Conflict (IAC) in Colombia is Internal Forced Displacement (IFD), a feature with few spatial economic analyses. The Colombian case is highly relevant for this type of research because (a) the country’s geographic and environmental diversity has determined its development path (Safford and Palacios, 2002) and the unfolding of the IAC itself (Rangel, 1998), and (b) Colombia has the largest number of forcefully displaced people in the world. This article introduces a theoretical framework of the relationship between municipal development and IFD, and tests it in a 2000–10 semipanel for 1042 municipalities under two different sets of spatial controls: spatial econometrics and topography controls.

Information Society

This article reviews and assesses the concept of the ‘information society.’ It considers six analytically separable definitions of an ‘information society’ (technology, culture, economic, spatial, occupational, primacy of theoretical knowledge). These conceptions are each reviewed and criticized in turn, as a means of highlighting the varying ways in which major social thinkers have approached the subject. The general concern of thinkers to explain change—as more about continuity than discontinuity, or vice versa—is addressed through contrasting analysis of ‘postindustrial society’ and ‘informationalized capitalism.’ Central authorities discussed are Daniel Bell, Manuel Castells, and Herbert Schiller.

Regional Science

2.4 Other

Brief mention of some other work completes a broader picture of regional science. RSAI has awarded its Founder’s Medal to Martin Beckmann for research in transportation network theory, location theory, and spatial economics ; William Alonso for urban economics, spatial demography, and regional development; Jean Paelinck for methods of regional analysis, spatial econometrics, and regional policy; and David Boyce for transportation network models and urban transportation planning methods. The three most cited articles in the International Regional Science Review are on population dispersal from major metropolitan regions internationally (D. Vining 1978), intra-urban residential mobility (J. Quigley and D. Weinberg 1977), and the shift of the US population to nonmetropolitan areas (C. Beale 1977). In the Journal of Regional Science they are on the distribution of residential activity in urban areas (J. Herbert and B. Stevens 1960), determinants and consequences of migration (M. Greenwood 1985), and an efficient algorithm to solve the Weber location problem (H. Kuhn and R. Kuenne 1962). In Regional Science and Urban Economics, they are on the baby boom and the housing market (N. Mankiw and D. Weil 1989), the demand for housing characteristics (J. Follain and E. Jimenez 1985), and publicly provided inputs and economic growth (T. Garcia-Mila and T. McGuire 1992). The supplementary Bibliography includes well-cited books and recent overviews of aspects of regional science.

Spatial Thinking in the Social Sciences, History of

2.2 Space as Distance

Economics had developed another approach to analyze the role of space in the functioning of society. In the early nineteenth century, some economists were unhappy with the elimination of space from mainstream theory. Hence, the development of spatial economics as a sideline. It was centered on two problems: the uneven distribution of natural factors and the role of distance. The name of David Ricardo is associated with the first orientation and the ensuing theory of international specialization and trade. Johann Heinrich von Thünen explained how distances to the market determined farming specialization even in a perfectly uniform setting (1826–52). The progress of spatial economics was a slow one: people had to wait until Alfred Weber for a coherent theory of industrial location (1909), and Walter Christaller and August Lösch for an understanding of the location of service activities and the nature of urban networks (early 1930s).

At the end of the nineteenth and the beginning of the twentieth centuries, some anthropologists also considered distance as a major factor of cultural differentiation. It soon appeared that diffusionism could not serve as an overall explanatory theory. Diffusion was, however, a significant cultural process (see Anthropology, History of ).

When geographers, tired from the hopelessly descriptive character of the possibilist approach, tried in the late 1950s and 1960s to develop a more systematic view of their scientific enterprise, many of them decided that geography should become a discipline of distances: they incorporated what spatial economics and anthropology could offer and went further through the emphasis they gave to communication processes. They used what a new scientific field, the study of communication, was developing.

The functional approach to social problems through the analysis of distance was fruitful. Its role in planning theory and practice was essential during the 1960s and 1970s. It allowed for the design of well organized cities or suburbs. It did not prevent, however, the development of urban pathologies. In order to understand them, other approaches to the role of space in social life had to be developed.

Geographic Thought, History of

2.4 Geography as Explanation: The Social Science Approach

Many geographers tried to give a new start to their discipline. The most successful was Edward Ullman (1912–1976). His diagnosis was clear. Human geography failed because it was conceived as a natural science. Overemphasis had been given to the study of human beings/milieu relationships. Circulation, the other face of human geography as defined at the end of the nineteenth century, had been neglected. In order to build a modern geography, emphasis had to be given to social interaction (Ullman 1980 ).

Geographers discovered that economists had developed an interest for spatial economics since the beginning of the nineteenth century. They had explored the influence of distance upon the location of farming, industrial activities, and services. Geographers decided to take advantage of the results of spatial economics to give a more systematic dimension to human geography. It was the basis of the new geography of the late 1950s and 1960s.

In a way, this new geography translated Ritter’s position into social terms. Instead of focusing on the opposition between inland and coastal locations, for instance, it focused on the dialectics between center and periphery. The center was defined in terms of social physics as the point or the area that enjoyed the highest accessibility to the totality of a social group. Procedures were invented to measure centrality and to map it. The idea of population or income potential changed the traditional vision. Instead of the center as a point, it presented the center as an area. In the United States, a central core, the manufacturing or industrial belt, appeared as the main feature of the territorial structure.

Geographers relied on spatial economics to develop their new understanding of human geography, but they went far ahead of economists in their analysis of spatial organization. The regional geography of the first half of the twentieth century fell short of a systematic formulation. Thanks to the works of Edward Ullman, Torsten Hägerstrand (born 1916) and a few other geographers, the way space was organized—through specialization, the development of networks, the dynamics of scale and external economies, and the opposition between center and periphery—became clear.

During the 1970s and 1980s, a few geographers, for instance, Paul Claval (born 1932), tried to extend the analysis of spatial interaction to non-economic aspects of social life, either social relations or political structures. Instead of focusing on transport costs, they analyzed communication, the structuring influence of commutation costs, the role of medias, and the significance of symbolism. In this way, they provided a new understanding of the dynamics of globalization, so evident from the 1960s.

The emphasis on social interaction had positive effects on geography in many respects, but it reduced the interest in the ecological approach at a time when ecology came really of age and pollution became a major problem at the local, regional, and global levels.

Resource Geography

2.1 Resource Inventory and Evaluation

The stock-in-trade of resource geography has traditionally been the identification and analysis of resource distribution at spatial scales from local to global (e.g., Murphy 1954 ). For example, descriptions of natural resource distribution conventionally form a central component of regional geography, where they provide a logical segue between the description of a region’s geological, hydrological, pedological, and biological characteristics and discussion of the region’s socioeconomic geography. Using this spatial mapping approach, resource geographers have quantified natural resource endowments, evaluated the capacity of particular natural resource bases to support economic development, and determined the extent to which current strategies of resource acquisition, conservation, and environmental protection are adequate.

By providing assessments of the spatial extent, utility, and economic viability of mineral, biological, and hydrological resources, resource geography has often played the role of handmaiden to regional planning and economic development. Early examples from within US geography can be found in the watershed and irrigation survey work of geomorphologists such as Grove Karl Gilbert ( 1877 ) and John Wesley Powell ( 1879 ). Their evaluations of water resources in the arid western states in the second half of the nineteenth century helped promote the federal government’s involvement in dam construction, irrigation, and inter-regional water transfer systems. In the UK, the development of the national Land Utilization Survey by Dudley Stamp ( 1934 ) is indicative of how resource geography has historically contributed to land-use planning and, more generally, to the implementation of a conservationist utilitarianism towards natural resources. Professional affiliation between resource geographers and public resource agencies continues to generate empirical resource inventories and practical approaches for implementing integrated resource and environmental management (see Mitchell 1997 ).

The strategic and military value of mineral and energy resources has often provided an overtly political focus to the work of resource geographers, confirming MacKinder’s ( 1887 ) maxim that geography serve as an aid to statecraft. For example, resource geographers served as part of a multidisciplinary team of social scientists compiling a series of Area Handbooks commissioned by the US Department of the Army (see www.hqda.army.mil ). These ‘compilations of basic facts’ describe economically and militarily significant aspects of selected countries including identification and inventory of natural resource endowments. Whether part of regional or applied geography, the primary objective of local, regional, and global resource appraisals is informational. As a consequence, this line of inquiry has produced maps of resource location, atlases, and descriptions of resource availability, but has contributed little in the way of theoretical or methodological development.

GIS Applications for Socio-Economics and Humanity

3.01.2.1.1 Nonstandard spatial statistics and spatial econometrics

Just like statisticians in general, spatial statisticians tend to favor a data-driven approach, resulting in the notion of letting data speak for themselves. This viewpoint reflects the sampling design basis of statistical inference. Accounting for all patterns in a dataset is a crucial feature of this approach, which frequently couples data detrending with assumptions to allow residual values to be treated as independent and identically distributed (iid) random variables. Conspicuous data trends may be attributable to covariates, spatial autocorrelation, or a combination of the two. Complexity associated with spatial data analyses often is a function of noisy (e.g., considerable dispersion), dirty (e.g., nonlinear relationships), and/or messy (e.g., unbalanced factors) properties of collected data. Experience with Box–Cox power and Yeo–Johnson transformations and the normal-linear statistical model reveals that specification and assumption flexibility, and/or robustness is needed in order to properly address this complexity.

In contrast, just like econometricians in general, spatial econometricians often favor a theory-driven approach to data analysis, resulting in a theoretical model specification being confronted by data. Model specification (and its suite of diagnostics), parameter estimation, and hypothesis testing frequently are the primary building blocks of a spatial econometric model, emphasizing these features over others (e.g., the data collection sampling design) that a spatial statistician might consider as, if not more, important. Because empirical economics furnishes evidence that complexity tends to characterize spatial economic data, a spatial economist prefers to concentrate on the complex patterns of a functioning spatial economy during the model specification phase of an analysis.

Given this context, Griffith and Paelinck (2011, p. ix) tabulate a summary of similarities of and differences between spatial statistics and spatial econometrics. Similarities include: employing model-based inference, properties of estimators, specification of geographic neighborhood structure, the modifiable areal unit problem (MAUP), quantifying spatial autocorrelation, variable transformations (e.g., Box–Cox, Manly, Box–Tidwell, Yeo–Johnson), spatially adjusted statistical techniques (e.g., ANOVA), geographic cluster detection, distance as a covariate, Bayesian analysis, exploratory spatial data analysis (ESDA), and space–time model specification. Meanwhile, on the one hand, spatial statisticians have a keener interest in design-based inference, the ecological fallacy, spatial interpolation, missing spatial data imputation, nonnormal auto-probability model specifications, spatially varying coefficients, Bayesian smoothing, and error propagation. On the other hand, spatial econometricians have a keener interest in constrained parameter estimation, optimization models, endogeneity issues, and spatial regimes.

Extending this discussion to the general aspects previously mentioned, Griffith and Paelinck (2011) also illustrate differences in the nature of data studied. The first part of their book, addressing spatial statistics, contains more maps than does the second part, addressing spatial econometrics. Content in the second part of their book relies more on space–time data and classical regression-based techniques. Names for spatial autoregressive models furnish a good illustration of terminology differences. Spatial statisticians tend to refer to the first-order geographic dependence model as a conditional autoregressive (CAR) specification, the second-order geographic dependence model with both a spatially lagged response and covariates as a simultaneous autoregressive (SAR) specification, and the second-order geographic dependence model with only a spatially lagged response variable as an autoregressive response (AR) specification. In contrast, spatial econometricians tend to refer to spatial autoregressive models with the acronym SAR, the SAR specification as a spatial error model (SEM), and the AR specification as a spatial lag model (SLM). Another example here is Haining’s (1990) presentation of added variable plots methodology from statistics to select spatially lagged terms in a model specification, which spatial econometricians essentially refer to as the spatial Durbin model ( Mur and Angulo, 2005 ).

Another conceptual difference between spatial statistics and spatial econometrics pertains to their respective perspectives about a model specification’s error term. The principal foundation for spatial statistical inference is a term constituting either sampling error (design based) or stochastic error (model based). In contrast, spatial econometrics views the error term as a surrogate for such complications as missing variables (e.g., a poorly specified regression equation) and assumption violations (e.g., endogeneity, heteroskedasticity). Accordingly, they tend to be interested in robust standard errors. Meanwhile, a commonplace contention is that “one scholar’s mean response is another scholar’s variance term.” This statement characterizes one of the differences between spatial statistics, which tends to spotlight variance, and spatial econometrics, which tends to spotlight treatment effects. The variance focus is exemplified by geostatistics, in which modeling variance is central, and the devising of spatial sampling designs, whose goals are to better control and minimize sampling variance. In keeping with this perspective, spatial autoregression specifications model the inverse geographic variance–covariance matrix. The treatment effect focus is exemplified by the use of dummy (i.e., indicator) variables, frequently to the point of overparameterizing (i.e., saturating, such that a specification contains a separate parameter for every possible combination of data values that a given set of covariates can have) a model.

Spatial statisticians often deal with observational data, and hence conduct correlational studies that can focus on only covariations, and not on cause-and-effect. The critical issue concerns whether or not confounding factors lurk in the background of an investigation; an analysis cannot distinguish between the effects of two covariates on a response variable. In many situations, an experiment cannot be conducted (perhaps for ethical or practical reasons) to control for these variables (e.g., the spillover of cancer risks from second-hand smoking). Spatial statisticians have alternative approaches for causal inference, including: (1) collecting a preponderance of circumstantial evidence from many studies; (2) establishing a conceptual framework whose logic supports cause-and-effect contentions; and (3) designing quasi-experiments. In contrast, spatial econometrics approaches causal inference by distinguishing among three contexts: (1) establishing hypotheticals and/or counterfactuals with theory, (2) identifying causal model parameters with hypothetical population data (i.e., large sample inference); and (3) identifying causal model parameters from actual data, which involves sampling variability. A spatial statistician tends to rely on randomization to control for confounders, whereas a spatial econometrician tends to rely on instrumental variables, or variables that do not belong in a model specification that are correlated with a set of covariates and seek to induce changes in them without directly affecting a response variable, and that are uncorrelated with the error term.

A final source of difference discussed here concerns the scope of quantitative methods embraced. Spatial statistics employs the full range of probability theory coupled with univariate and multivariate statistical techniques (e.g., Pebesma, 2004 ; Sain and Cressie, 2007 ) to analyze point patterns, and attributes attached to either point or polygon data. In contrast, spatial econometrics, which is not simply spatial economic statistics, focuses far more on linear regression techniques, often overlooks multivariate techniques, and has more of a time series analysis emphasis.

In conclusion, spatial statistics and spatial econometrics exhibit numerous similarities. Many of the differences noted in this section do not involve mutually exclusive content. Rather, they highlight dominant themes in these two disciplines.

The changing role of government: transformation

The changing delivery environment

In this emerging transformed public sphere, who is in the full delivery chain and what are their roles? Public policy can be delivered by a mix of elements: the state and its bureaucracy, in the shape of central, regional and local government, and its agencies; companies operating in the market, in which private enterprise and investment is harnessed to deliver public goods; and the community, in the shape of participating citizens and civil society organisations. The relative proportions of the mix, and the relationship between the elements, depend upon the prevailing values and philosophy of the time; from the post-war development of the state-backed welfare state to a renewed role for the market from the 1980s onwards. Many commentators believe we are now in the midst of another sea change in the balance of relationships (e.g. Kaletsky, 2010 ; Hutton, 2010 ; Blond, 2009 ).

The role of the state

The state has for some time been shifting from direct delivery of public services to the commissioning of delivery by others – ‘steering not rowing’, to use the 1990s phrase. The argument in favour is that central planning and delivery of services, as a monopoly, is inherently wasteful and inefficient, slow and cumbersome in responding to a changing world, and that departmental managers develop ‘empire-building’ instincts that makes them resistant to change. Therefore introducing elements of competition and choice is necessary to drive improvements in performance and operational efficiencies through market-style disciplines. The state may retain a regulatory oversight role, with a lighter or heavier touch, or shape the environment in which competitors operate through instruments such as legislative change, fiscal rules or public opinion.

The state will always oversee some kinds of services directly, for example:

where all its citizens are guaranteed rights to a minimum level of access to fundamental public goods, such as secondary education and healthcare;

where there is no business case that is attractive to private enterprise and the market fails;

where government must provide the basic infrastructure for other services to develop;

where issues of equity and justice are the most important concerns, such as in civil justice;

where the security of the state and its citizens is paramount, such as defence.

The move towards the market as the means for innovation has, in many areas, also been accompanied by a devolution of powers away from the centre to the regional and local level. The result is that service delivery has been outsourced, left to competitive markets or devolved to local or regional organisations, and central government departments that previously managed volume processes focus instead on regulating those markets, setting the conditions in which they operate or managing them through financial controls. Records management has, similarly, been devolved to those organisations and coordination lost.

The role of the market

From the 1980s onwards the idea of the market as a driving force in change has steadily grown in influence, affecting either the way that services are structured or the manner in which they are managed. Some examples are:

privatising of previously state-owned assets, such as railways, and the provision of privately funded infrastructure, such as toll roads and bridges, with ‘light touch’ regulatory regimes;

introducing elements of internal markets into universal state services: such as patient ability to choose between competing healthcare providers, retaining a central state funding that ‘follows the patient’;

importing private sector management practices into the public sector, particularly in the ‘new public management’ movement;

setting performance targets and payment-by-results systems;

allowing open access to government data.

Most recently, though, the 2008 crises have led to a re-examination of the relative roles of state and market, and the way in which both relate to the individual as both citizen and consumer. On the one hand, this leads some towards renewed calls for regulation in areas where the impact of failure in one market has exponential implications for others (as in the banking industry, for example). This strand recognises that government differs from the market in some fundamental ways, for example:

While private enterprise is profit-seeking, government is responsible for creating public value and more general public goods, such as economic growth or social cohesion.

It is responsible for balancing public value across the community, both geographically – for example, in regional development – and socially.

It remains accountable for the delivery of others, even though it has no direct control over them, and takes the blame when things go badly wrong.

The Gov 2.0 example illustrates this debate: should government develop and supply information services directly? Or should it make the data which it collects (and only government is capable of collecting much national scale data effectively) freely available to all comers, whether private sector companies building chargeable services, social enterprises working on a non-profit basis or enthusiastic individuals distributing free applications? Should government work out for itself what is needed, attempt to specify and commission detailed information- based services, building them itself or directing what is produced competitively, or simply provide a platform of data, without attempting to guide the shape of services that emerge, relying on the inventiveness of others to fill the need with various offerings and consumer choice to determine which will thrive?

Government as a platform (Gov 2.0)

The idea of government as a platform emerged from the experience of Web 2.0, in which general purpose products, such as Google Maps, offer a common platform on which many more applications can be quickly built. ‘Mash-ups’, for example, combine open data – spatial, economic, statistical – from different sources maintained online, building them into innovative new products.

This approach stresses experimentation, rapid evolutionary development, open systems and learning from users.

‘Gov 2.0’ argues that government should follow this lead, providing open standards and mechanisms for making the substantial datasets which it collects freely available. This lets companies, non-profit organisations, other agencies and interested individuals develop interactive interfaces, particularly as applications for personal and mobile devices, in ways that government would never have thought of. It should leverage this option in preference to spending on complex, long-term development of its own big systems. Some examples are: •

The Apps for Democracy initiative makes the contents of its data catalogue of the Washington, DC, administration available as open public data; for example, real-time crime incident feeds; school test scores; local poverty indicators.

After the Massachusetts transport system made real-time bus arrival time data available, third party developers built more than a dozen applications for mobiles, smartphones, SMS and an LED display within two months.

The UK Government has committed to establishing a public right to data and is making various datasets available for public use on data.gov.uk.

The World Bank makes its datasets in its data catalogue available via API access by default.

This example illustrates the underlying structural tension between innovation and regulation: innovation is vital, especially in a time of austerity, in order to modernise and transform existing institutions, whose size and complexity makes change difficult, but the responsibilities of government also require a concentration on stability and cohesion, keeping transformation within bounds which do not disrupt the goals of public policy or weaken trust and confidence in the state itself. Regulation must promote innovation, but the purpose of innovation is not for its own sake, rather to achieve social outcomes more effectively.

The role of the community

The search for an appropriate balance in the instruments of public policy brings the community to the fore. The term community is used here in a general sense, to include civil society organisations – charities and voluntary organisations – as well as those structured as mutuals or cooperatives, professional associations, local geographical communities, social networks centred on a particular interest or issue, and so on. Community is the space in which individuals and groups can cooperate together for mutual benefit, for themselves or on behalf of others, that lies between the hierarchy of state bureaucracies and the competitive terrain of the market. These kinds of groups interact with the delivery of services and social outcomes in various ways:

charities which contract with central or local government to provide a defined service, for example, citizens’ advice services;

mutualisation of public agencies, shifting from state- owned bodies to a cooperative, employee-owned structure, as an alternative to privatisation;

social enterprises working on a not-for-profit basis, for example, in developing environmentally sustainable industries to create local employment;

volunteer-based associations taking on specific local tasks;

participative and community-based budgeting in setting local spending priorities.

At one level, promotion of community engagement can be seen as an opportunity for the state to retreat from some areas of public service in the face of financial constraints; but it has deeper implications as well. The current interest in localism is a way of delegating responsibility, as well as decision-making powers, whilst still retaining a guiding control – keeping control of the size of the purse, but allowing others to distribute whatever is available. This strategy follows modern management practice in locating decision- making on an issue with those who have best local knowledge of it, since they are closest to the problem, at the same time as constraining the boundaries of action. This is attractive to central government, often overwhelmed by the huge amount of local detail which must otherwise be processed in a central planning strategy, and to politicians and elected officials, who are then well-placed to take credit for enabling achievements but also to shrug off failure as the fault of others.

A higher profile for community as a locus of policy action is also attractive as a means of addressing those complex policy issues which represent the greatest challenge. Broadly, at the core of their resolution lie two kinds of change, which are linked:

changes in behaviour at the individual level: for example, in reducing energy use, tackling the obesity epidemic or levels of anti-social behaviour;

changes in perception of problems: for example, measures which reduce the actual incidence of crime do not seem to change perceptions at the same time – against the evidence, a majority believe that crime in general is actually increasing in the UK overall, except in their own area.

The localism agenda – aiming to empower communities to have a greater say on local issues – is complemented by an emphasis on personal rights and responsibilities, which requires the individual to take more responsibility for doing things for themselves. In part, this is increasing choice by providing alternatives to state monopolies, where the market does not wish to step in; and in part, it is both requiring and empowering (which takes precedence depending on your point of view) to make either/or choices for themselves and their area: a healthier lifestyle versus (possible) co-payment for treatment of avoidable illness; better community facilities versus lower local taxes, or a contribution of personal time and effort to run alternatives.

Marketised services encourage a consumerist attitude: for continually improving quality and access at no greater cost; community-based participation calls for co-production of results – clients and practitioners, citizens and service providers, working together to produce value – or co-payment for the increased costs of service improvement. Competition and choice encourage the consumer; but now the money has run out and the big issues do not respond well to that approach – and so the citizen must be engaged as well.

The role of public manager

The effect of these trends is to increase the number and range of different actors involved in the delivery chain – public and private sector, charities and community groups, clients and professionals – and to decrease the amount of direct control which public administrators can exercise. What is the role of the public manager in this emerging landscape?

The traditional role for the administrative decision-maker in bureaucracies was to ensure that the public interest predominated, by advising on the making of policy choices from a position of expertise and reason, and to implement policy by devising and operationalising a set of standardised rules under which all cases received the same treatment; to separate politics from administration. This is the tradition in which the disciplines of records management evolved and which is reflected in the kind of system requirements for EDRM, which the European Union MoReq2 project typifies.

The new public management (NPM) movement reoriented this role by the introduction of a customer services model. The role of the public manager is to convert policy choices into market (whether internal or external) choices and to assess relative value on the basis of cost-benefit calculations. This role gave precedence to the professional manager over the practitioner as the principal decision-maker in allocating resources: measuring what could be measured, setting targets, managing programmes and monitoring achievement. The professional manager is an agent of the political will, held to account through performance contracts and ensuring that it is not interpreted through the lens of specialist practitioner interest.

While business efficiency remains central, the emphasis is shifting towards the practitioner as implementer: those – head teachers, medical practitioners, local residents – most engaged with day-to-day delivery on the ground, bringing decision- making, value-creation and accountability closer to the point of production. In this context, a new role is added to public management, as expert facilitator rather than expert policymaker: to work within systems of governance to uncover the range of issues and problems, to seek out common values among all the interests involved, to articulate alternative programmes of action for producing public value and to guide the choice between them. In some ways, this turns things on their head: from overseeing and implementing policy choices from above, to working up from problems on the ground by building consensus on future action. Rather than knowing the right answer, knowing how to work out feasible alternatives and choose between them is the most important skill.

Simulation models applied to crops with potential for biodiesel production

4 Other models applied to crops with potential for biodiesel production

The generic models aim at widening the range of applicability of crop simulation models. Although each of these models has been widely applied, they strongly reflect a specific modeling community and the degree to which models have been applied outside the community of the developers, can mainly be attributed to the accessibility of the specific model and the efforts to disseminate the model by the team that was involved in its original development. Despite of more specific and simple agronomic models lack of flexibility and only evaluate a reduced set of management alternatives, they have proven to be useful for the analysis of many crops, mainly those with potential for biodiesel production.

Table 2 summarizes some studies on crops with potential for biodiesel production in which specific simulation models were developed or applied. Studies like these should motivate the development of models for other crops, mainly the non-conventional ones which have potential for biodiesel production, as well as the adaptation of the existing models with the purpose of evaluating different crops.

Statistical and empirical models have been developed to identify the factors that influence crop changes, and predict future crop patterns upon changes in driving factors as specified in scenarios. Multiple linear regression or logit models are frequently used for this purpose. Meneses (2007) presented a statistical model to predict the castor bean yield for state of Ceará (Brazil) as function of the precipitation observed during its vegetative and reproductive cycles. Comparisons shown that the model results agree well with the observed ones along the years for the entire studied region. Lapola et al. (2009) modeled the potential productivity of sugarcane and physic nut in India e Brazil. Land requirements for such expansions were calculated according to policy scenarios based on government targets for biofuel production in 2015. Spatial variations in the potential productivity lead to rather different land requirements, depending on where plantations are located.

A system to forecast soybean crop yield for Brazil was evaluated, based on regional empirical models to assess crop yield, with data from a national database of soybean cropped area in municipal scale, and from an agrometeorological monitoring system covering all Brazilian States. Soybean yield was estimated for harvests from 2000/2001 to 2005/2006 and compared to Conab (National Supply Company) surveys. Good fittings (R 2 > 0.87) were gotten for region grouped yield data ( Assad et al., 2007 ). Another model of soybean yield was developed, integrating the major climatic, edaphic, and economic determinants in the Amazon Basin. Yield was modeled as a function of yield as simulated by a crop physiology model that captures the effects of climate and physical attributes on the development of soybean plant; fertilizer applications; and economic/spatial parameters such as credit, transports costs and latitude. Current values of these determinants indicated that roughly 20% of Amazon Region, excluding protected areas, can generate yields greater than 2000 kg/ha ( Vera-Diaz et al., 2008 ).

A simulation model was developed for canopy photosynthesis and dry matter accumulation in oilseed rape based on the ecophysiological processes and using a three-layer radiation balance scheme for calculating the radiation interception and absorption by the layers of flowers, pods, and leaves within the canopy. Testing of the model for dynamic dry matter accumulation through field experiments of different genotypes, sowing dates, and nitrogen levels showed good fit between the observed and simulated data, with an average root mean square error of 10.9% for shoot dry matter ( Tang et al., 2009 ).

Kantolic et al. (2007) varied the genetic coefficients accounting for photothermal requirements and photoperiod sensitivity of soybean A5409-RG (maturity group V), reducing pre-flowering phase while maintaining time to maturity. The model produced sound estimates of crop phenology and reproduced the positive relationship between seed production and the duration of the critical period between first and last pod found in field studies. In a similar study, Setiyono et al. (2007) developed a soybean phenology model (SOYDEV) which utilizes non-linear temperature and photoperiod functions and separates floral induction and post-induction for simulating time of flowering. This model accurately simulated the dynamics of vegetative development, final node number and the occurrence of major reproductive stages such as flowering, beginning pod, mid-pod elongation, beginning seed, and physiological maturity in a 6 years field experiment.

In another work, Setiyono et al. (2008) developed a sink-driven approach and evaluated it for leaf area index simulation in soybean under near-optimal environments. Data for model development and evaluation were obtained from irrigated field experiments conducted at two locations in Nebraska, where agronomic management was optimized to achieve growth at a near yield potential level. The proposed model has minimum input requirements. Interactions between leaf growth and sourcedriven processes can be incorporated in the future, while maintaining the basic physiological assumptions underlining leaf expansion and senescence.

Adaptations of models based on physics, chemistry and mathematics are also found in literature. Müller and Diepenbrock (2006) adapted a generic steady-state gas exchange model to leaves and pods of the cruciferous species oilseed rape. Parameter–nitrogen relationships were established to account for effects of organ senescence and nitrogen content. Components of dark respiration related to photosynthetic and to non-photosynthetic tissue of siliques were treated separately. A two-step calibration and validation approach relying on light response curves and diurnal time courses of CO₂ exchange and transpiration rates measured on leaves and pods was used paying special attention on robustness, reliability, and universality of model parameterization. Simulations corresponded well to the observations, indicating that the model can be used to study and quantify eco-physiological patterns of gas exchange of both leaves and pods of oilseed rape.

Soil water flow and nitrogen dynamics were simulated in sunflower field during and after the growing period, in Northern Greece by using a one-dimensional simulation model based on the Galerkin finite element method. The authors examined the effects of irrigation with reclaimed waste water and nitrogen fertilizer applications on plant growth, water and nitrogen distribution in the soil profile, water and nitrogen balance components and nitrogen leaching to groundwater. The model simulated the temporal variation of soil water content with reasonable accuracy, but an over estimation of the measured data was observed during the simulation period ( Rahil and Antonopoulos, 2007 ).

Li et al. (2009) developed a model capable of predicting boll maturation period and simulating cottonseed growth, protein and oil content driven by the inputs of cultivar parameters, weather, and crop management variables (N fertilization). The model structure was based on the processes of biomass accumulation, N uptake and fat synthesis in cottonseed. The responses of cottonseed growth and quality formation to weather or crop management were quantified, respectively. The field data set conducted in a wide range of climate conditions with different cultivars of cotton planted on different dates was used for model validation. The result showed that the model is robust enough to accurately predict cotton boll maturation period, cottonseed biomass, protein and oil content.

Papageorgiou et al. (2009) presented a new modeling and simulation approach based on the soft computing technique of fuzzy cognitive maps to address the issue of crop yield prediction. This soft computing technique is an advanced knowledge representation and processing method that can handle the main characteristics and site specific management behavior of the cotton crop yield providing an interpretable and transparent model. The main advantage of the proposed decision making tool in precision agriculture is the sufficient simplicity and interpretability for farmers in decision process, which make it a convenient consulting tool in determining cotton production.

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