On the Micro-Foundations of Creative Economy: Life Satisfaction and Social Identity

: Incorporating the unavoidable changes manifesting in the behavior of individuals as an outcome of evolution at the level of development of the economy and society, we pinpoint a new theoretical background requiring the reconﬁguring of the micro-foundations of economic theory regarding the creative economy. We thus set the theoretical micro-foundations of the creative economy where the main microeconomic incentive lies in life satisfaction attainment rather than utility maximization. At the macroeconomic level, we propose the pairing of sustainability with the creative economy. The objective is met through the vehicle of social identity derived from the responses to a questionnaire distributed in Greek society. Through principal components analysis, we locate two sets of variables shaping life satisfaction: the exogenous inﬂuences within social identity (state of health, age, and level of education) and the endogenous primary inﬂuences within social identity (lending, cultural change, and happiness). Each one of these variables is determined by other interpretative variables. Furthermore, we observe a weakness of economic policy to inﬂuence life satisfaction in advanced Western societies, resulting in a paradox of the economic toolkit. Our ﬁndings are relevant for policymaking to promote life satisfaction, especially within the creative economy context.

Introduction
The organization of economic science has been largely based on the neoclassical approach of economics. By definition, this theoretical framework assumes that the motivation behind action is the maximization of utility. However, an individual's behavior receives stimuli from a synthetic portfolio of psychological and non-materialistic needs. Hence, the concept of utility maximization as the central motive of human behavior might not always apply in the case of advanced societies where individuals have met basic survival needs. In advanced societies, not only individual but also societal values shift toward the embracement of sustainability [1].
A sustainable model of production-at least in developed economies-requires the redefinition of behavioral patterns and priorities of individuals and societies to attain sustainability and quality of life. Sustainability and quality of life are not necessarily associated with utility maximization from consumption and, inductively, to the materialistic underpinnings of life. After some point, it is safe to assume that individuals and societies are motivated by the goal of life satisfaction attainment.
When investigating notions such as happiness, life satisfaction, and altruism, which do not fall under the traditional approach of economic motivation, the researcher should approach them outside the neoclassical framework or, at least, by substantially modifying its assumptions. Accordingly, the diversification of the big picture concerning the prevailing macroeconomic paradigm by introducing the notion of sustainable growth requires Sustainability 2022, 14, 4878 2 of 17 miscellaneous analytical tools for the interpretation of economic behavior at the microeconomic level at first. Thus, in the light of sustainability as the primary macroeconomic goal, the adjustment of the micro-foundational requirements of the economic analysis is urgent.
For the above-mentioned reasons, we suggest a re-conceptualization of the microfoundations of macroeconomics for a sustainable economy, which is tightened with creative economy. In our analysis, life satisfaction [2] is the basic driver of human behavior rather than utility. Life satisfaction consists of an individual's comprehensive judgment of its quality of life [2]. The assessment of the shaping factors of life satisfaction occurs through the vehicle of social identity theory [3]. Social identity theory is proposed as an analytical tool because it incorporates contextual factors and personal and social characteristics. Evidently, subjective well-being-a component of life satisfaction-is affected by personality traits and the overall environment of an individual [4].
Hence, we recommend life satisfaction interpreted by social identity as an alternative primary objective of the individual and society at a micro-level. The methodological approach followed in this study is derived according to the dictates of the evolution of the production system toward the creative and knowledge economy, seeking a corresponding microeconomic theoretical foundation. This proposition is in line with scientific and social ripeness and acceptance by scholars of the notions of happiness and well-being [5].
For the empirical analysis, we used answers from a printed questionnaire, which was distributed in Greek society from 2019 to 2020. To derive the shaping factors of life satisfaction, we applied principal components analysis. Our evidence suggests that life satisfaction is primarily shaped by variables such as happiness, state of health, cultural change, lending, age, and level of education. In the sequel, we examined each of these primary influences to locate the secondary influences of life satisfaction.
Our findings are particularly useful for targeted policymaking for the promotion of life satisfaction. In this direction, policies should focus on the promotion of stability, trust, and cultural factors that respect the traditional aspects of the cultural background. In terms of economic policy, it seems that a paradox of the economic toolkit arises, as our findings suggest the absence of pure monetary incentives besides the preference toward state intervention to reduce income inequality.
This study aims to ground the theoretical foundations to approach, in the future, the structural characteristics of the creative economy as well as its entrepreneurial aspects, which might involve the concept of intellectual capital as an alternative to physical capital. Furthermore, it would be of interest an attempt to develop a microeconomic model of human behavior in which life satisfaction is set as a primary goal.
The remainder of the article is structured as follows: In Section 2, we present the concept of the creative economy. Section 3 concerns life satisfaction as a micro-foundation of the creative economy, while in Section 4, the notion of life satisfaction is discussed per social identity theory. Section 5 is devoted to the empirical analysis. In accordance, Section 6 analyzes how altered priorities are translated to altered identities. Finally, Section 7 regards policy implications and direction for further research.

Toward a Creative Economy for Sustainable Development
Sustainable development and the related idea of sustainability are reinforced under the notion that human civilization could be organized to promote ecological and social welfare [6]. It usually includes the attainment of the global goals regarding poverty, health care and education provision, well-being, reduction of inequality, climate action, etc. Thus, sustainable development encompasses a broader agenda for action for people, prosperity, planet, peace, and partnership, promoting better policies for better lives [7,8].
Given that the global economy is approaching several ecological and transformation tipping points, this generates an imperative search for a new vision regarding alternative economic futures for the global economy. In tandem, this process consists of theoretical limits in regard to the traditional approach of economic theory. In the present study, we propose the notion of the creative economy as one of the compelling sectors for sustainable economic growth.
The analysis of the relationship between creative economy and sustainable development is rather complicated due to the various definitions of creative economy and the involvement of many areas of economic activity [9]. Nevertheless, creative economy is closely related to sustainability, especially by promoting urban economic growth [10]. In this direction, creative economy regards the rejuvenation of cities as cultural centers for the enhancement of cultural identity and economic vitality [11,12]. This process generates investment opportunities, which substantially contribute to economic growth and life quality. Besides, urban sustainability is tightly related to the pillars of economic, social, and environmental sustainability [12].
Creative economy may yield significant economic and cultural benefits to society, generating distribution networks for its activities [13]. This "interconnections" effect is the outcome of the symbiotic and cooperative relationships within creative economy where growth in one part of the economy prompts growth in another part [14]. Hence, creative economy is understood as an ecosystem that combines a range of new professions activated by creativity [14]. Under such a definition, creative economy may be applicable to several economic sectors and activities, such as the entrepreneurial, educational, health, cultural, and financial sectors, concerning any kind of economic activity that originates from research and development activities.
One of the main characteristics of creative economy is the accumulation of human capital, which increases economic effectiveness and productivity [15]. Creativity produces value either in the form of economic value or in the form of intellectual property rights [16]. Concerning intellectual property rights, new ideas and knowledge are founded on intellectual capital [17]; hence, creativity operates as a factor of production [18] with a dynamic character.
In this study, the suggestion of the creative economy as one of the determining factors of economic growth concerns the observation of the behavioral shift in almost all industrial economies from traditional values to secular and rational values [19,20]. Once a society has experienced a stage of industrial development, it emphasizes post-materialistic values related to self-expression rather than survival values [20,21]. Hence, values within a society generate cultural change due to historical path dependence [20,22]. Besides, natural and cultural capital are parallels evolving simultaneously [23].
As mentioned above, there is difficulty locating the generalized links between creative economy and sustainable development; hence, it is more appropriate to focus on individual performance within the economy [9]. That said, the placement of the goal for life satisfaction attainment symbolizes the shift of the individual priorities and hence behaviors. This approach does not cancel the neoclassical background of utility and profit maximization. By contrast, its contribution to economic growth is acknowledged for the generation of comparative advantages and promotion of a certain level of life quality. However, as advanced societies move toward climate change adaptation, where capitalism as we know it is facing criticism, the assumption of an individual gaining utility only by (over)consumption might be falling.
In other words, when we set sustainability as the main goal of economic activity profit and utility, maximization goals, which are founded on monetary incentives, are being (at least partially) replaced. Hence, macroeconomic sustainability is not consistent with the neoclassical approach. At this point, it is more rational to assume that individuals and societies value and are motivated toward life satisfaction rather than utility.
On the other hand, creative economy differs from an economy based on physical capital investment and is far from the traditional concept of knowledge economy concept, as it requires the existence of intellectual property rights and satisfactory behavioral rules as its basic characteristics; thus, it might serve as a suitable alternative at a post-materialistic level with individual goals, such as the salient motivation toward the attainment of life satisfaction and other post-materialistic values. Even though the historical path is not to be ignored, in this study, we focus on the interpretation of human behavior using social identity theory to determine what influences life satisfaction. Thus, we set the theoretical framework of the above-mentioned analysis by empirically testing it outside the merely mathematical approach of a classical economic model.

Life Satisfaction as a Micro-Foundation of the Creative Economy
In microeconomics, the traditional neoclassical behavioral assumption is rationality and the general motives toward utility or profit maximization under monetary constraints. More specifically, the equilibrium is the "opposition between tastes and the obstacles for their satisfaction" [24,25]. These assumptions allow a simplification of the analysis and provide a powerful analytic framework. Outside neoclassical modeling, the agents are not similar even though they may share a range of common behavioral characteristics.
The question regarding the need to change the micro-foundations of the dominant paradigm in economics in both microeconomics and macroeconomics is not original. In the past, some great thinkers such as Veblen [26], Galbraith [27], and Myrdal [28] kept this question open. During the 1970s, this debate was intensified due to the emergence of the first generation of environmentalists, whose thinking was occasioned by the scarcity of primary materials [29] and the energy crisis. However, once the economic and consequently productive circumstances shifted in the direction of the service-provision economy, the intensity of this debate was de-escalated.
Nowadays, the theoretical discussion regarding the effectiveness of economic theory to demonstrate the economic reality has been reinstated under the demands of the deteriorating conditions of climate and technological change and the validation of the initial theories of Maslow [30] and Menger at a later point [31]. Hence, when high levels of economic growth are attained, individuals aim for the fulfillment of post-materialistic needs that stem from cognitive bias, self-esteem, and self-realization. In the present study, we propose that this shift of behavioral priorities comprises a new theoretical framework motivated by the need for the accomplishment of life satisfaction.
Is now established that at the macroeconomic level, the gross domestic product (GDP) is not well-designed to evaluate social welfare, as it is mainly a measure of the market and of monetary wealth [32][33][34]. A plethora of contributing factors to boost GDP levels are associated with lower levels of well-being, such as expenditures relating to crime, pollution, and poor health [35]. An updated critique from the OECD's High-Level Group on the Measurement of Economic Performance and Social Progress underscores the point that GDP was not designed to provide a proxy for both economic and general welfare [36].
By examining the qualitative characteristics of life, concepts not directly related to monetary gains emerge. These circumstances are advocators for life satisfaction-and concern notions such as self-realization-and are placed at the center of the analysis [37] mainly due to the improvement in living standards. A confirmation of this hypothesis lies in evidence that individuals tend toward the consumption of post-materialistic goods, such as entertainment, sports, and wellness services, when their income rises satisfactorily [38].
A priori, the redefining of the production structures from the viewpoint of sustainable and creative economy accords with the establishment of life satisfaction as a microfoundation for an individual's behavior. The motive for life satisfaction emphasizes postmaterialistic elements, which infuse meaning into the individual's life. For this to occur, contextual factors are critical, too. For example, in the case of an open economy where tolerance, trust, civic engagement, and non-materialism coexist, societal happiness and life satisfaction are promoted [39].

Life Satisfaction, Social Identity, and the Creative Economy
Social identity theory approaches human existence by investigating the environmental influences under which the individual lives and acts [40]. Thus, social identity is the outcome of social influences [41] as well as personal emotional factors [42] leading to the individual's self-categorization [43] as regards various social roles. Since there are a plethora of social groups, there are a plethora of social identities, which, in most cases, operate functionally concerning one another [44].
To locate the expression of social identity, it is required to determine the motives prompting individuals to belong to groups and acquire social identities. The formation of groups and societies emerge due to the exposure of individuals to shared problems requiring cooperation for their solution [45]. Uncertainty activates human behavior [46], and social identity construction is motivated under the need for the reduction of uncertainty for the self and society [47]. This is a dynamic process due to the evolution of societies and the concomitant evolution of values and aims to alter the nature of the shared problems. As a deduction, social identities have dynamic features as well to correspond with the requirements in question. Due to such characteristics, social identity theory is an appropriate measure of behavioral shifts as identities, unlike single-peaked utility preferences, change over time to reflect altered individual and societal goals.
The incorporation of identity into economics concerns the involvement of the social decision mechanisms and how a person's sense of self affects economic outcomes [48]. Social identities are then reflected in economic choices and pose significant constraints, besides income, that might affect individual well-being [48] and life satisfaction. That said, consumption patterns and preferences are tightly linked with social identities [49]. In terms of social contracts that are based on consumption, such as exchanging gifts, objects bear the identity of the giver and satisfy greater societal needs and values [50,51]. Hence, there are emotional components among customer preferences, consumption patterns, and companies that are entirely affected by social identities and self-identification processes [52][53][54][55].
Salient social identities determine both the channels of consumption and production. Regarding the latter, businesses and firms that aim at strong relationships and consumer satisfaction receive stimuli from social identities and modify their products and service to establish consumer loyalty and satisfaction besides the fulfillment of basic utilitarian needs [52]. Henceforward, societies and economies evolve and so do social identities and consumption patterns [49]. On these grounds, a shift in post-materialistic values will accordingly lead to shifts in social identities and, of course, consumption patterns.
In this study, we highlight the transition process from identities oriented to basic needs to identifies oriented toward life satisfaction. In other words, in advanced economies and societies, individuals are motivated by life satisfaction rather than economic utility, which is reflected in their consumption patterns as well. This hypothesis holds for an individual's stage in life where the growing needs of survival have been met [30]. Therefore, individuals seek life satisfaction once they have attained a secure environment [56] and have fulfilled needs relating to materialistic values, which allows them to move onto post-materialistic values [57]. This fact may pose a new theoretical framework to the prioritization of the individual preferences concerning life satisfaction [58].
On these grounds, the more post-materialistic the culture, the lower the overall level of entrepreneurship based on physical capital [59]. Similarly, higher levels of life satisfaction indicate a negative role in entrepreneurship [59]. In this study, we suggest that the role of the creative economy is crucial to bridging the negative relationship between post-materialistic economies and entrepreneurship due to its structural characteristics. Perceiving the shift of cultural values as a shift in social identities and goals at the microeconomic level [60], the networking character of the creative economy and the investment in intellectual capital contribute to the maintenance of identities and values and economic growth [49]. Moreover, the emergence of the creative economy is the expression of fundamental social and political changes, which can be approached by the incorporation of identity shifts [60]. Furthermore, it is found that individuals employed in creative occupations have higher levels of life satisfaction [61].
Life satisfaction concerns the feeling of enjoyment based on an abundance of subjective criteria [62]. The character of life satisfaction is shaped by three determinants: (a) the individual's characteristics as well as influences by the family environment, (b) the shifts in the individual's values and/or priorities, and (c) changes in social values that concern the family, pro-social behaviors, and materialistic values [63].
The determining factors of life satisfaction concern the relationships of individuals and the role of the state and institutions; hence, they are directly related to social identities. At the same time, life satisfaction-just like economic utility-is a self-regarding notion allowing for its interpretation with respect to social identity. Besides, life satisfaction is achieved as the outcome of satisfying major psychological needs through the acquisition of social identity via participating in groups [64].
It is comprehended that life satisfaction is not identical to happiness although, together, they comprise the perception of well-being. Well-being is a result of five independent components: (a) positive emotion, (b) engagement, (c) relationships, (d) meaning, and (e) accomplishment [65]. Genuinely, these five elements are by definition related to social identity as well.
The above-mentioned concepts are of critical importance in terms of policymaking, as the attainment of positive feelings, such as happiness, is at the center of economic growth and development [66], while states with salient characteristics promote life satisfaction [67,68].

The Construction of Social Identity and the Empirical Analysis
In this section, we present the methodology employed, the empirical results, the discussion of the empirical results, the exogenous and endogenous influences on life satisfaction, and finally, the analysis of the endogenous influences.

The Methodology Employed
Social identity-like utility in neoclassical economics-is a self-relevant approach [69] to the analysis of individual behavior, and hence, its attestation occurs through the use of questionnaires [70][71][72].
Hence, the point of departure for the empirical analysis is the description of social identity through 30 selected variables, which correspond to 30 questions in a questionnaire distributed to Greek society from 2019 to 2020. The field research was assigned to a survey bureau member of the European Society for Opinion and Market Research (ESOMAR) and the Market Research and Public Opinion Companies' Association (SEDEA). The gathering of the data regarded household interviews using a printed questionnaire, and the sample choice was made proportional to the population size sampling. The sample consisted of adult females and males from all over Greece with sufficient knowledge of the Greek language. Further information for the descriptive statistics is available in the Supplementary Materials section with the presentation of the variables of social identity.
The number of observations is 1305. The variable of life satisfaction is the targeted variable. Thus, the question posed is how and to what extent the 29 independent variables affect the dependent variable of life satisfaction. In this manner, we determined life satisfaction's characteristics and determining factors.
For the empirical analysis, we used principal components analysis (PCA). This methodology dates from Pearson [73] and Hotelling [74] and is appropriate for the simplification of large data packages, as it reduces the dimensionality of such data sets [75]. In general, PCA is widely used by behavioral science researchers to access the dimensional structure of data and for data-reduction purposes [76]. Furthermore, PCA has been used to examine aspects of social identity [70][71][72]77] as well as aspects of social capital [78].
The empirical analysis comprises the following steps: Firstly, we applied PCA on the 29 variables of Supplementary Materials. The variable of life satisfaction as excluded from the analysis since it is the targeted variable. Secondly, the principal components (PCs) were treated as independent variables for the interpretation of life satisfaction through regression. From the statistically significant PCs, we selected those with a loading > | 0.30 | and ran regression. The final interpretative model of life satisfaction consists of statistically significant variables.

The Empirical Results
It was found that life satisfaction is determined by social identity by 65.43%. The determining factors are six in number: (a) happiness, (b) state of health, (c) cultural change, (d) lending, (e) age, and (f) level of education. Of these, state of health, age, and level of education are exogenous influences within social identity, while happiness, cultural change, and lending are endogenous primary influences within social identity (Figure 1). The interpretation of life satisfaction as determined by social identity is presented in Table 1 below.
change, and lending are endogenous primary influences within social identity (Figure 1). The interpretation of life satisfaction as determined by social identity is presented in Table  1 below.

Demographics
Age (+) Level of Education (+) The variables happiness and state of health exert immediate effects on life satisfaction, as they influence the individual's current state of being. In general, the concept of

Age (+) Level of Education (+)
The variables happiness and state of health exert immediate effects on life satisfaction, as they influence the individual's current state of being. In general, the concept of happiness is a more short-term notion than life satisfaction [79]. Happiness precedes many positive life achievements [80], constituting a leading measurement for life satisfaction [81].
The sign of the variable state of health indicates that a poor state of health constitutes a negative influence on life satisfaction. A priori, good health is a necessary precondition for a person to enjoy life [82,83], with physical and mental health being two of the main determinants of life satisfaction [84]. Apart from physical health, mental health plays an important role in the attainment of life satisfaction [85][86][87]. In general, positive mental health includes happiness, and therefore, it indicates playing a positive role towards life satisfaction [88]. As the research was conducted both in 2019 and in 2020, a negative sign for the variable state of health was expected due to the COVID-19 pandemic, which was a primary source of uncertainty and a threat to human health and life. Consequently, apart from the effects on the physical health of the people infected by the virus, COVID-19 also affected mental health due to a rise in uncertainty and anxiety [89].
The variable cultural change entered into the analysis of life satisfaction with a positive sign, suggesting that a favorable attitude toward cultural change positively affects life satisfaction. In general, culture yields important information about individuals' quality of life and well-being [90], and it is related to societal emotional environment, which, of course, differs among cultural backgrounds [39].
Cultural change is a rather different concept to culture [91,92]. However, if cultural change positively affects the societal emotional environment, it will positively influence life satisfaction as well. Thus, it is safe to assume that individuals are positive towards culture change when it enhances values that promote friendly cultural circumstances toward life satisfaction.
The variable of cultural change as a contributing factor to life satisfaction should be highlighted as it is, nevertheless, impressive given that the prevalent psychological theories usually emphasize the importance of stability, which is linked to stable personality traits and parental influence [63]. Our findings regarding cultural change point out a more radical aspect in social behaviors, which might be linked to more specific socioeconomic characteristics of Greek society after the ten-year economic recession [93] and, possibly, to the above-mentioned changes preempted by the COVID-19 crisis [94].
The variable lending positively affects life satisfaction, highlighting the relationship between unofficial credit access and life satisfaction, which has been analyzed in the past [95]. Unofficial microfinance availability impacts one's well-being beyond the access to capital and monetary outcomes, as it rescues uncollateralized insolvent borrowers from exclusion. Thus, it restores their dignity, self-esteem, and social recognition and, consequently, has a positive impact on life satisfaction. Furthermore, credit access is essential for the improvement of life quality and a necessary determinant in reducing poverty and overcoming difficulties generated from exogenous shocks, such as the COVID-19 pandemic [96]. Hence, credit access is an important factor for the promotion of sustainability as well.
Our findings suggest that the variable age positively affects life satisfaction. In general, research has found a U-shaped relationship between age and life satisfaction, which means that both young and older people are more satisfied with their life than middle-aged individuals [97]. An explanation for this U-shaped form is that it reflects the key events at different stages of life (career, children, marriage, etc.). By contrast, according to socioemotional selectivity theory [98,99], greater emotional saliency motivates people to regulate their emotions to maintain higher levels of subjective well-being and thus life satisfaction. The latter statement is in line with our sample.
Regarding the variable level of education, a positive influence on life satisfaction is noted. Education could be approached as a consumption good that contributes to better living conditions [100,101]. The consumption value of education goes beyond present consumption [102], and thus, as life satisfaction is a long-term notion, the positive relationship is no surprise. Furthermore, returns in education are associated with employment opportunities that offer higher earnings [103,104] and hence the opportunity to shift consumption patterns toward post-materialistic goods and services [38].
Consequently, life satisfaction is shaped by two-thirds of the sum total of influences deriving from social identity and the rest by influences exogenous to social identity, which are not covered in this study.

Exogenous and Endogenous Influences within Social Identity
Having analyzed the basic influences from social identity on life satisfaction, we further distinguished them as (a) exogenous influences within social identity and (b) endogenous primary influences within social identity. The naming of the two groups of variables Sustainability 2022, 14, 4878 9 of 17 concerns how they influence social identity construction and saliency and do not refer to statistical properties.
Exogenous influences within social identity are state of health, age, and level of education. This group is considered "exogenous" influence to social identity due to the fact that these variables constitute virtually objective situations. The use of the word "virtually" was chosen because, in a questionnaire, the true state of things may be different from what the respondent believes to be the case. Admittedly, there are questions with no margin for misinterpretation. We accept, then, that these variables are independent components of social identity and are not being influenced by emotional factors and self-categorization processes. Thus, note that these variables reasonably affect all the rest of the life satisfaction variables and do not require further analysis.
The second source of endogenous primary influences within social identity includes happiness, cultural change, and lending. All three have a positive sign regarding life satisfaction and concern beliefs and attitudes of individuals that require further analysis regarding their determining factors. Hence, these three basic variables of social satisfaction appear to be of much greater importance and meaning in a manifest way than what they seem, which we will discuss below.
That said, from this point on, the analysis will proceed on two levels. The first level of the analysis concerns the determining variables of the endogenous primary influences (happiness, cultural change, and lending) ( Table 2). The second level of the analysis regards an overview of all the determining factors of life satisfaction (Figure 1).

Further Analysis of the Endogenous Influences
For each of the endogenous primary influences (happiness, cultural change, and lending), we created the correlation matrix without, of course, including the dependent variable of life satisfaction or any of these variables. However, the exogenous influences within social identity (state of health, age, and level of education) are included in the correlation matrices. In each correlation matrix, we located the variables with correlation coefficients > | 0.10 |. This was deemed necessary, as we did not want to exclude any variable.
In the sequel, we ran regressions with these variables. We distinguished the statistically significant variables from the final regression. The results from the final regressions are presented in the Table 2 and in Figure 1. We can attribute the shaping of life satisfaction to three factors, namely (a) exogenous influences within social identity, which are state of health, age, and level of education; (b) endogenous primary influences within social identity, which are happiness, cultural change, and lending; and (c) the residuals, which are determined outside our statistical model. For the endogenous primary influences within social identity, by means of the correlation matrices, we located the secondary influences that affect them. These are the variables of adventure-seeking, surprises, creativity, income status, tradition, trust, the role of migrants, political self-definition, and inequality and the state. These variables either concern personal attitudes or attitudes on social relationships, both expressed by social identity.
The analysis of the endogenous primary influences within social identity brings into light three observations. Firstly, the variable of lending is interpreted with a coefficient of determination of less than 5%. However, it has two statistically significant interpretative variables that intuitively accord with the nature of the lending activity and credibility. The higher the income status, the higher the lending ability of an individual [105]. The negative role of the age of an individual regarding its credit access corresponds to the fact that the social support system (including access to lending) may decline with age [106].
Secondly, the coefficient of determination of happiness is 15.43% (relatively low) even though the independent variables that define it are statistically significant. Hence, happiness is primarily shaped by influences outside social identity. This conclusion is particularly plausible considering the short-term character of happiness. In degree of influencing happiness, three sets of variables determine happiness: the exogenous influences within social identity (state of health and age), the positive secondary influences within social identity (inequality and the state, trust, and adventure-seeking) and the negative ones (surprises and creativity).
The fact that the state of health and age have a negative sign is explained based on the impact of the COVID-19 pandemic. Hence, this appears to justify the negative sign in the short-term variable of happiness. In the contrast, the positive sign of age in terms of life satisfaction explicitly states the long-term nature of life satisfaction.
Under uncertainty, mainly due to the two back-to-back crises (Great Recession of 2008 and the COVID-19 crisis) [93], the role of the state to regulate the economy was highlighted [107][108][109]. Hence, the positive sign of the variable inequality and the state points out the above-mentioned fact, as it states that the government should take measures to reduce income inequality. Besides, a reduction in income inequality promotes happiness [110,111].
The positive sign of the variable trust indicates that sufficient levels of social trust promote happiness. Especially, trust within a society may affect the level of individual happiness without passing the economic channel [112]. The positive sign of the variable political self-definition on happiness indicates that individuals who possess a political affiliation with the right are more prompt to profess as happy [113].
Another important source of happiness concerns the variable of adventure-seeking. Given that happiness is defined as feeling excited [114], this finding applies to the short-term nature of the variable of happiness. By contrast, the negative sign of surprises influencing happiness concerns unpredictable events that may be positive or negative. The variable of surprises bequeaths uncertainty in the analysis of happiness; hence, the negative sign does not contradict the positive sign of adventure-seeking, as the former is planned rather than random.
Finally, the negative sign of the variable creativity-representing one's need to be creative-suggests a negative influence on happiness. This finding has been noted before [77] and opposes the general impression and intuitive expectation of a positive relationship between creativity and happiness and, as a deduction, life satisfaction [115]. Furthermore, because creativity is a statistically significant variable, it represents an interpretative challenge.
It is reasonable to assume that creativity oriented from monetary incentives rather than artistic is a requirement for happiness and life satisfaction due to its expected dynamic returns. However, the short-term nature of happiness is not outcome-oriented, undermining the need to be (entrepreneurially) creative. This hypothesis poses a broader understanding of the role of creativity in the long-term regarding life satisfaction. Life satisfaction is a desirable state of an individual's life occurring during the prevalence of post-materialistic values, meaning that the individual could be less driven toward (economic) creativity and entrepreneurship. Thus, a negative relationship between the variables was established.
Thirdly, cultural change posing a dynamic factor for life satisfaction formation is interpreted by 36.28% by both exogenous influences within social identity (state of health) and positive secondary influences within social identity (tradition) and negative secondary influences within social identity (role of migrants and political self-definition).
The negative sign of the variable state of health regards a negative opposition to cultural change. Given that cultural behaviors have important implications for human health [116], and culture, in terms of epidemiology, is a socially transmitted system of shared knowledge, beliefs, and practices varying across groups [117], it is rational to assume that individuals experiencing health problems hesitate toward cultural change that might alter or/and burden the functionality of the health care system, which is an attitude probably accelerated due to the COVID-19 pandemic.
Another determining positive force for cultural change is the variable of tradition, stating that society perceives cultural change favorably only if it is respectful toward tradition. This particular evidence explicates the awareness of cultural contexts as a critical factor for the functioning of a society. This finding is in line with the fact that tradition is a salient feature of social identity, which individuals thrive to defend against a threat [118]. A similar picture applies to the variable role of migrants. Finally, it is applicable for political self-definition to negatively relate with cultural change, as the two notions express opposing views to life. Political self-definition likely relates to stability, which, of course, does not combine positively with cultural change.

Conclusions: Altered Priorities Are Translated to Altered Identities
In this study, we established that life satisfaction is shaped primarily by social identity features. Specifically, two groups of variables come into play in this: (a) the exogenous influences within social identity (state of health, age, and level of education) and (b) the endogenous primary influences within social identity (lending, cultural change, and happiness). Each one of the above-mentioned variables is determined by other interpretative variables. Hence, it is of great interest to categorize the interpretative variables into two large subgroups: (a) those expressing attitudes on social relationships (trust, role of migrants, political self-definition, and inequality and the state) and (b) those expressing personal attitudes. Within personal attitudes, we further distinguished the variables expressing a dynamic character (adventure-seeking, surprises, creativity, and income status) and a traditional character (tradition). The attainment of life satisfaction presupposes the coexistence of both these characters.
We thus pose the theoretical framework for a synthetic behavioral portfolio based on the evolution of social identities to incorporate the needs and values of each historical period. Theoretically "normalizing" the notion of time to zero, we focus on the "new era" of human behaviors. Based on the prevalence of post-materialistic values in advanced economies, we propose that life satisfaction attainment is a much more consistent goal than the utility-maximization hypothesis. The proposed behavioral goal of life satisfaction is consistent with sustainability and also with the creative economy. On these grounds, the creative economy offers a much more appropriate macroeconomic background for economic growth.
The vehicle for the transition to a sustainable creative economy regards social identity, which incorporates individual and social priorities in its formation. In this case, for a smooth transition process, individual priorities are required to move in the same direction.
Hence, in a post-materialistic economy where life satisfaction attainment is the central microeconomic goal, altered microeconomic priorities translate to altered identities to embrace the common goals for sustainability.

Policy Implications: Toward Life Satisfaction and Further Research
Radical changes result in behavioral shifts [119,120], and our analysis is especially relevant for understanding the impact of radical changes and demands on human behaviors. A representative example of a radical change in the priorities of societies is climate change and the need to protect the planet in the direction of a sustainable production structure linked to sustainable behaviors [119,120]. This influence concerns behavioral portfolios for the promotion of sustainability at an individual and organizational level of the firm [121][122][123]. In the latter case of the firm, this regards the alteration of the organizational structures to ensure the firm's resilience [121], innovation and creativity [121,122], and the employees' loyalty and identification with the firm [123]. That said, the economic future of the next decade will be shaped within a broad but modified paradigm of capitalism, in which the notion of sustainability may replace profit maximization [109]. Thus, the pursuit of sustainability requires the adoption of new methodological tools and micro-foundations related to the altering of human behaviors.
This attempt corresponds to locating social identity through the pinpointing of motives that serve as indicators of preferences [124]. Thus, social identity could be auxiliary means for understanding the role of the individual during the transformation processes for a green economy [125]. Hence, groups with salient identity features that promote sustainability could act collectively to alter the social system to embrace the values of a green economy. The reason behind this lies in the fact that although group needs are not completely identical to individual needs, the two kinds of needs are linked in similar directions in matters concerning well-being and life satisfaction [126].
In general, the social identity analysis for the emergence of effective policymaking has been an object of research [48,127,128]. Our study provides suggestions for promoting life satisfaction through targeted policymaking. Social-policy-favorable interventions concern influencing the dynamic character of individual preferences by mitigating the impact of negative surprises through increasing the predictability of political systems. In general, fostering the feeling of trust could favor the levels of societal and individual life satisfaction. Furthermore, it is of critical importance for policymaking to respect the traditional aspects of the cultural background while simultaneously raising security levels. Under this notion, attention needs to be paid to the management of the issue of migration because it may likely have negative repercussions.
Another aspect to highlight is that in advanced Western societies, there is weakness of economic policy to influence social life satisfaction, as only income status appears as a secondary influence of social identity. Thus, it seems that in these societies, despite the option of affecting the equal distribution of income through the role of the state, affecting the variables purely relying on monetary incentives, such as motive for wealth accumulation, will not drastically influence social life satisfaction levels. In other words, a comparative irrelevance of economics to influence social life satisfaction emerges, which requires further analysis due to the coming up of important issues for economic policymaking and politics constituting a "paradox of the economic toolkit".
Our study could be placed in the framework of a contemporary evolutionary theory of economic change as founded by Schumpeter [129] and Alchian [130] and subsequently formalized by Nelson and Winter [131], particularly referring to human individual motives of economic action. The difference in the theoretical approach lies in the fact that, up until now, evolutionary perspective took a Darwinian [132] and a Neo-Darwinian [133] view of human and entrepreneurial behavior.
In the present study, without denying the macro-Darwinian background for the need for survival of an individual and the ramifications concerning the survival conditions of the planet (climate change and biodiversity loss), we perceive the unavoidable changes manifesting in the behavior of individuals as an outcome of evolution at the level of development of the economy and society. Consequently, we pinpoint a deeper theoretical and real cause that requires the reconfiguring of the micro-foundations of economic theory.
For our approach to mature theoretically, it requires further research in four directions: firstly, in the analysis of the structural characteristics of the creative economy and the posing of challenges for a new political economy of growth and, secondly, in the analysis of the economic and entrepreneurial aspects within the creative economy that set aside profit-maximization goals and are sustainable and resilience-oriented. Thirdly, future research should proceed in the direction of analyzing the theoretical and empirical consequences of the modification of the primary targets of individuals and enterprises. A central characteristic of this process involves the concept of intellectual capital [17], which may replace or act complementary to physical capital in a range of activities and economic sectors. In the end, it would be of interest to develop a microeconomic model of human economic behavior in which life satisfaction rather than utility is set as a primary goal of individual behavior after the first period of its life. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Furthermore, the statistical service that was assigned the questionnaire project provided us all the legal documents.

Data Availability Statement:
The analytical tables of the empirical analysis are available at Supplementary Materials.


The Importance of Scale and the MAUP for Robust Ecosystem Service Evaluations and Landscape Decisions

: Spatial data are used in many scientiﬁc domains including analyses of Ecosystem Services (ES) and Natural Capital (NC), with results used to inform planning and policy. However, the data spatial scale (or support) has a fundamental impact on analysis outputs and, thus, process understanding and inference. The Modiﬁable Areal Unit Problem (MAUP) describes the effects of scale on analyses of spatial data and outputs, but it has been ignored in much environmental research, including evaluations of land use with respect to ES and NC. This paper illustrates the MAUP through an ES optimisation problem. The results show that MAUP effects are unpredictable and nonlinear, with discontinuities speciﬁc to the spatial properties of the case study. Four key recommendations are as follows: (1) The MAUP should always be tested for in ES evaluations. This is commonly performed in socio-economic analyses. (2) Spatial aggregation scales should be matched to process granularity by identifying the aggregation scale at which processes are considered to be stable (stationary) with respect to variances, covariances, and other moments. (3) Aggregation scales should be evaluated along with the scale of decision making (e.g., agricultural ﬁeld, farm holding, and catchment). (4) Researchers in ES and related disciplines should up-skill themselves in spatial analysis and core paradigms related to scale to overcome the scale blindness commonly found in much research.

Introduction
Spatial scale-the spatial scale of measurement or in geostatistics, spatial supporthas huge impacts on spatial analyses, model outputs and, thus, process understanding. The impacts of scale are well understood in quantitative social sciences to the point where any research in this domain is expected to be able to describe the impacts of their choice of aggregation scale on their analysis, results and derived understanding [1]. However, little land use research and related studies of the goods and services provided by land based systems such as agricultural production, biodiversity, flood protection and other elements related to concepts of Natural Capital (NC) and Ecosystem Service (ES) has considered the impacts of spatial data scales on their analyses. In fact, there are many examples of blindness to the analytical impacts of scale, where processes captured at one scale are applied to another without considering the inferential impacts of these differing scales. For example, Spake et al. [2] applied forest models captured over stands (a specific spatial unit in forestry) to 10 km gridded data and Finch et al. [3] used a nutrient delivery model constructed over a 50 m grid to make inferences on 1 km squares. Such scale mismatches affect the robustness of the results and have implications for the reliability of any policy or planning recommendations arising from them. This paper seeks to highlight the importance of considering and evaluating the impact of scale using a hypothetical ES optimisation problem. In so doing, it addresses this key methodological gap in current approaches to landscape, land use, NC and ES evaluations related to scale and specifically the impacts of the Modifiable Areal Unit Problem or MAUP [4][5][6].
In brief, MAUP posits that statistical distributions, relationships and trends exhibit very different properties when the same data are aggregated or combined over different reporting units at different spatial scales. It describes the process of distortion in results or models that is driven by the aggregation scale and generates differences in statistical relationships. In one of the few examples of research that has examined the MAUP in the context of ESs, differences of up to 329% were found when timber production was quantified using data at different spatial scales [7]. For geographers, MAUP is a core consideration when working with spatial data but one that is frequently overlooked by the many disciplines now routinely using such data and making recommendations to decision makers. Through a land use optimization problem, this paper seeks to illustrate the challenges related to analyses of spatial data and to show how representations and understandings of landscape processes are fundamentally affected by MAUP. It shows the need for landscape decisions to take account of scale in order to provide robust evaluations of, for example, NC, ESs or climate-resilient land uses.

Background
Ecosystem service evaluations are inherently linked to the spatial data of land use and social and environmental factors. ES evaluations use statistical methods to quantify ES values and to suggest alternative spatial distributions of land use to enhance ES. Data may be from Earth Observation (EO) such as satellite imagery, from surveys and censuses (e.g., population, socio-economic and agricultural) or spatially interpolated data (e.g., soil, water and nutrient status). The inferential robustness of the outputs of models and analyses constructed in this manner is affected by uncertainties derived from the use of data with varying spatial scales. All spatial data (including EO data) are constrained by their spatial sampling scales or the spatial support of their measurements.
All analyses of spatial data are affected by spatial scale and, thus, the MAUP. Spatial scale encompasses spatial support and the spatial extent of analysis [8]. Spatial support is the area that each observation occupies-the space on which an observation is made-and is defined by size, shape, orientation and position [9]. For example, for an EO-derived land use dataset, the spatial support is the spatial resolution of EO data (e.g., 30 m Landsat image data). MAUP-related issues arise from the following: (1) the spatial support of spatial data and (2) the aggregation or interpolation of data to specific scales. The spatial support may be physically constrained but aggregation is not. Under MAUP, the spatial support of data affects statistically determined relationships between them, such as process modelling through some form of regression or characterisation through classification. Thus, statistical analyses of spatial data with different spatial supports will result in different model results, different correlations, different classifications, different coefficient estimates in regressions, etc. [1,5,10,11]. Some research [12][13][14] show the effects of varying spatial support: as support changes (up-scaling and down-scaling or aggregation and disaggregation) statistical and other relationship changes. MAUP is a core geographical consideration. Some domains have extensively investigated the consequences of ignoring its effects, particularly demographics [15] and epidemiology [16], but it is often overlooked in landscape systems modelling or has only recently been discovered [17,18]. In the context of ESs, NC and land use decisions, the support of spatial data affects patterns identified in any analysis for a given spatial extent (e.g., farm field, farm holding and catchment). As of yet, little work has considered the impacts of varying spatial supports in the analyses and evaluations of ESs. Yet MAUP's implications for any landscape or land use evaluation framework are profound: it results in divergent evaluations [7], mis-specification of ES trade-offs and synergies [17] and the mislocation of hot spots [19] compounding an already difficult task of identifying locally appropriate land use scenarios [20].

Methods
This paper uses a land use allocation case study to explore the impact of scale on evaluations of ESs. It provides a novel demonstration of the effects of scale, spatial support and MAUP. A vector land use data set was extracted from commonly available data, which contains real field boundaries and real land use classes. These were aggregated to a set of regular grids at different spatial resolutions. A set of hypothetical ES gradients (grids) were also created at different spatial resolutions. The problem used to exemplify the impacts of scales was the need to reallocate current land use for the study area in order to optimise the delivery of benefits under a single ES.

Data
Land use data were created from the UK Land Cover Map 2015 vector layer [21] and the Land Cover plus: Crops (2018) layer both at 1:25,000 scale and downloaded from the EDINA Environment Digimap Service (https://digimap.edina.ac.uk, accessed on 16 April 2019). These were merged to ensure that the crop layer populated the agricultural land cover classes and a 2 km square area in the Vale of Belvoir in the East Midlands of the UK was extracted. The original land cover, land use, habitat or land type-the terms are used interchangeably here-were reclassified by using the look-up table in Table 1, as shown in Figure 1.  It is common for land use optimisation analyses to work with gridded data at different scales. Land cover and land use data are frequently derived from remote sensing at different resolutions from medium scale (10-50 m) to coarse scale (500 m to 1 km). An example includes the UK Land Cover maps that are available as a vector product (as in Figure 1), as a 25 m raster grid [22] and at 1 km summaries [23] To evaluate the impacts of variations in scale, a series of land use optimisations was undertaken using the land use data in Figure 1 and aggregated to 40, 50, 67, 100, 200, 400, 500 and 1000 m resolutions, some of which are illustrated in Figure 2. These resolutions were chosen to illustrate a range of aggregation scales. The land use class for each grid cell was determined from the field data in Figure 1   In ES analyses, different land use types are conceptualised as supporting any given ES to differing degrees. In this study, ES scores were assigned to each land use class as in Table 2. These were derived from a local ES study [24] and are broadly related to some sort of regulating ES. In order to examine the interactions of land use with ES, an ES gradient, ES, was also defined. This has greater intensity in the south and the east and uses a nonlinear decay function to create ES values: where X and Y are the relative locations of each grid cell on a scale [0, 1]. The resulting ES gradient was aggregated over different scales, as shown in Figure 3. These surfaces could represent Provisioning, Regulating or Cultural ESs, such as Soil Fertility, Flood Risk or Wilderness, respectively.

Land Use Optimisation
The aims of the analyses described and undertaken below is to determine land use configurations that maximise ES provision to support some land use allocation decision and to examine how these are affected by data aggregation scales. In the allocation, the counts of the existing land use objects-the field parcels in Figure 1 or the grid cells or pixels in Figure 2-were retained, and the task is to identify the best set of locations for each land use object. This is a high dimensional search space: Each land use object can be allocated to each location, which in the case of the 200 m grid with 100 cells requires 9.3 × 10 157 permutations to evaluate and in the case of the 100 m grid with 400 cells involving 6.4 × 10 868 permutations.
A Genetic Grouping Algorithm (GGA) was used to explore this search space. GGAs were first proposed by [25] as an extension of classic Genetic Algorithms (GAs).
Optimisation with a GA first creates a set of potential solutions or chromosome, which are composed of genes, which in this case represents a set of specific locations (grid cells or fields) for specific land uses. The chromosomes or individual is then evaluated using some fitness criteria to assess the performance of the individual. Genes (locations) from successful individuals are interchanged in a crossover operation which combines two chromosomes to create new individuals, which are, in effect, offspring or children. To avoid stagnation, some gene mutation is introduced and, in this manner, the idea behind GAs is that they breed optimal solutions by creating and selecting fitter generations and, hence, the analogy with natural selection: Crossover creates new chromosomes from successful ones, and mutation ensures diversity. GGAs are a modification to classical GAs. They evaluate groups or subsets of individuals rather than only individuals and maintain group membership during the crossover operation for members that may have been displaced by other operations. GGAs have been found to be suited to location problems [26] due to the manner that GGAs approach the search space and the partitioning of potential solutions. Overviews of GGAs can be found in Comber et al. and Sasaki et al. [26,27], and comparisons of GAs and GGAs can be found in [28]. In this work, the algorithm described in Comber et al. and Sasaki et al. [26,27], itself based on the genalg R package [29], was further modified for land use allocation.
The key to any optimisation or search heuristic is the evaluation function. Here, this was to maximise the overall ES score. The reallocated land uses were evaluated by calculating the area weighted ES score using the land use specific scores in Table 2, the intersecting area weighted mean ES gradient values as in Figure 3 and the area of the field or grid cell as in Figures 1 and 2. Thus, the objective was to maximise the following: where i indexes the grid cells or fields G with allocated land use class, S is the ES score associated with land use as in Table 2, W is the underlying ES gradient value as in Figure 3 calculated from the area weighted mean of the intersection between the land use data (field or gridded) and ES gradients and A is the area of the field or grid cell in metres. Land use reallocation analyses were undertaken using field (vector) data in Figure 1 and gridded (raster) data in Figure 2. In this case, no new land uses were introduced, rather, existing land use counts could be allocated to different fields or grid cells. In all analyses, a degree of elitism was used in which the top 30% of the current population was carried forward to the next and the probability of mutation was set at 0.0002. Initial populations of 1.2× the number of observations (fields as in Figure 1 or grid cells as in Figure 2) were created, from which the optimised set of land use reallocations was bred to allow the selection of a stronger population. The number of iterations was set to 3000 in each case, which was sufficient to ensure convergence for each reallocation. In each analysis, observations pertaining to Urban, Broadleaf and Water land uses were held in fixed locations and not reallocated as these classes cannot be easily transposed to another location.
The ES gradients in Figure 3 were used to optimise (re-allocate) the spatial distribution of land use using both the field data in Figure 1 and the gridded land use data in Figure 2. Thus, the analysis sought to optimise the field data and gridded land use data aggregated to 50, 67 100, 200, 400, 500 and 1000 m, with ES gradients at 25, 40 50, 67, 100, 200, 400, 500 and 1000 m.
Full details of the code, the data and functions used to undertake the analysis and to generate and map the results can be found at https://github.com/lexcomber/lu_maup/.

Vector (Field) Land Use Data with Gridded ES Surfaces
A series of optimisation analyses were undertaken that allocated land use in Figure 1 using ES aggregations illustrated in Figure 3. The original and optimised ES scores generated from ES gradients aggregated to different scales are shown in Figure 4 for field (vector) data. The original and optimised ES scores on the y axis are the sum of the land use scores in Table 2, multiplied by the area weighted mean of the intersecting ES gradients illustrated in Figure 3 and the area of the land use parcel, as in Equation (2). Optimised ES scores are consistently higher than the original ES scores. The second feature evident in Figure 4 is the distinct peak in the optimised ES scores when they are evaluated using the 100 m grid. This perfectly highlights aggregation effects associated with MAUP, for which its effects are frequently non-nested and nonhierarchical and intrinsically related to the scales of assessment and evaluation. It is possible to hypothesise that the peak in ES score at 100 m may be due to the combined impacts of land use reallocation driven by the aggregation of the ES gradient. To unpack this a bit more, Figure 5 shows the proportions of the case study area occupied by reallocated land use and the contribution of each land use class to the overall ES score. Here, the influence of reallocating the Grass class is evident, particularly at ES aggregation scales between 67 and 200 m. Of the arable related classes that were subject to reallocation (recall that Urban, Broadleaf, Water and Coniferous land uses were not reallocated), the Grass class has the highest degree of ES provision, and this is reflected in reallocation across scales, where essentially Grass replaces Wheat. Interestingly, the peak for Grass proportions is not at 100 m but at 25 m and the effect of the ES grid resolution can be seen in the decline in Grass proportions after 400 m and an associated increase in Wheat allocations.
These scale effects are reflected in the different optimised land use configurations when undertaken using ES gradients at different scales ( Figure 6). This also reflects the effects of MAUP: the aggregated ES gradients at coarser resolutions are not sufficiently granular to 'pull' land uses with higher degrees of ES benefit towards the area to the south and east with high value areas of the ES gradient.
The results in Figures 4 and 6 highlight the importance of understanding the influence of aggregation scales in the way that processes are represented in models and the need to match process with measurement scale. This is a key point. For example, agricultural land use data recorded for each field parcel have an inherent grain where the fundamental unit of analysis is the field parcel itself. The concept of granularity or grain in this context includes the inherent properties of objects, which in the case of a field includes the boundary (in the UK fields are commonly demarked by hedges, fences and or ditches), access via gates, etc.

Aggregated Land Use Data with Gridded ES Surfaces
A second set of land use optimisation analyses was undertaken by using aggregated land use data in Figure 2 and aggregated ES data in Figure 3. The aim here was to examine the effects of aggregation scale when analysing land use data in a regular gridded format, as is commonly the case in many ES and land cover/land use analyses. Evidently, there is some information loss with conversion from vector data in Figure 1, as each grid cell is allocated to a single land use class. However, many land use datasets and analyses adopt this 'pixel' view of the world [30].
The optimisation scores using data aggregated to different scales are shown in Figure 7. Interestingly, the patterns are similar for each of the analyses: The results in Figure 7 strongly indicate that the variations in ES scores are generally being driven by ES aggregation rather than land use aggregations as similar trends are observed for each land use aggregation scale with ES aggregation as well as the trends in Figure 4. However, they also indicate the presence of scale related processes when ES gradients are aggregated to 100 m and 500 m that may be specific to this land use dataset and to these aggregation scales. These are also suggested in Figure 5. To investigate these, Figure 8 summarises the distributions of land use parcel perimeters (boundaries) and the square root of the parcel areas. It shows that the most frequent values in the distributions are land use parcels with an area of 10,000 m 2 (1 ha) and a boundary of around 500 m. These indicate the origins of peak ES scores when ES gradients are aggregated to 100 m and 500 m in Figure 7, and thereby the impact of the specific spatial configuration of the field parcels on ES evaluations. It is instructive to examine the allocated land use distributions. Figures 9 and 10 show optimised land uses for the land use grids at 100, 200 and 500 m, using all nine of the ES gradients aggregated at different scales. The same trends as in Figure 7 are evident: the overall efficacy of the optimisation and the ES score both decreased as the ES gradient aggregation scale increased. This is because as ES granularity decreases (i.e., aggregation scale increases), they are increasingly unable to generate ES scores for land use objects that allow them to be reallocated, for example, by pulling the Grass land use objects (with high ES scores) towards the southeast corner of the study area (the region with high ES gradient values).
It important to recall what is being shown and what is being performed with the data. Land use data are at single aggregated scale, as shown by the grid cells in Figures 9 and 10. The ES score for each object is generated from an area weighted mean of the intersection between the land use and the ES gradient. Thus, in Figure 10 there are 16 land use cells, for which its ES score is evaluated by 1600 ES gradient cells at 25 m (top left in Figure 10) but by four ES gradient cells at 1000 m (bottom right in Figure 10).
Consider the allocations in Figure 10 for the ES gradients aggregated to 25 m, 100 m and 500 m. These have the the same ES configurations but have different overall ES scores. They would have the same scores if they were evaluated over the same ES gradient, but they do not, as shown by the grid cell values in Figure 10. This illustrates the context of the better performance of land use allocations under the 500 m ES gradient in Figure 7

Discussion
The results of the case studies in this paper demonstrate the impacts of decision making on different scales of spatial aggregation and spatial support. The analyses reallocated land use in order to improve (optimise) the delivery of a single hypothetical ES over a small case study area. In this context, the land use reallocation and ES optimisation procedure illustrate issues associated with different scales of spatial support. A search heuristic (in this case a Grouping Genetic Algorithm) was used to optimise the spatial configurations of land use parcels in vector format and aggregated to grids of different resolutions. The land use reallocations and configurations were evaluated using an ES gradient aggregated over varying spatial supports and the GGA sought to maximise the overall ES score for the case study area. The aim of the analyses was to illustrate the effect of different scales of spatial support on decision making. For this reason, the case study was small and only a single ES was evaluated. In reality, ES evaluations were undertaken over larger scales (e.g., catchments) and considered multiple ES objectives (e.g., provisioning, cultural and regulating) and their interactions. To support such evaluations, ESs are commonly bundled [31,32] into groups of synergistic ESs, which co-vary to social and ecological pressures [33].
The results of this paper show first that, generally, as the scale of ES gradient aggregation increases (becomes coarser), the allocation evaluations are less effective due to the decreased resolving power and granularity of the ES gradient at increased aggregation scales. This overall trend was evident for land use in vector format ( Figure 4) and grid formats (Figure 7) when reallocated using ES gradients aggregated to different scales to evaluate potential allocations.
A second feature of the reallocation results was that the overall trends of decreasing ES evaluation with increasing ES gradient aggregation were interrupted when the ES gradients were aggregated to 100 m and 500 m, as shown in Figures 4 and 7 for vector and gridded land use. For this case study, the highest ES score results were generated under ES gradients aggregated to 100 m. There is the possibility that this non-hierarchical behaviour may be due to the arbitrary positioning of ES grids, although the same trends were observed in the analyses of both field (vector) and gridded land use data, suggesting that the agriculture-related processes in this study are best captured at that resolution.
Third, these trend discontinuities were shown to be due to the spatial interaction of the original land use data and the gridded ES gradients at these scales, as shown in Figure 8. This indicates that the "best" aggregation scale is driven by the spatial scale (support) of the underling case study data. It suggests that a different case study, containing field parcels with different typical spatial properties would be expected to show the same overall trends (of declining allocation effectiveness with increasing ES gradient aggregation) but with trend discontinuities at aggregation scales specific to case study data. However, this has not been substantiated in this study and suggests an area for further investigation.
Together, these results clearly indicate how decisions around land allocation and ES, whether at the field, grid, catchment or landscape scale, are mediated by the spatial support of data. The effects of the spatial support were shown to be nonlinear, non-nested and non-hierarchical and sensitive to the spatial properties and grain of the underlying objects (e.g., agricultural fields) and the data used to represent them in ES analyses (as shown in the trends in Figures 4 and 7). In turn, this makes the impacts of scale difficult to predict. It indicates that the evaluations of process spatial scale (in this case land use) should be undertaken at different scales of aggregation as part of any ES analyses and evaluation.
Extending this further, this also suggests that land use evaluations may benefit from locally varying scales of aggregation to reflect the spatial variation in the processes that are examined-in this case, land use. Figure 11 shows a quadtree representation of the data in Figure 1, suggesting how scales of aggregation could be locally determined to reflect locally varying spatial proprieties of the objects being evaluated. However, this is very much an area for further research.

Conclusions
There are a number of recommendations arising from this work for research that incorporates analyses of land use, ES and NC evaluations: MAUP should always be tested for. Any analysis of spatial data should routinely test for MAUP in order to understand the specific impacts of aggregations scales relative to the spatial support of the process being investigated. This is a common consideration in socio-economic analyses of spatial data [1] but has yet to be adopted in the ES domain and in work seeking to evaluate NC and to inform landscape decisions, land use planning and ES delivery.

2.
The scale of spatial data aggregations should be matched to the granularity of the processes being evaluated. This requires the identification of spatial scales at which the processes being investigated are considered to be stationary (stable) with respect to their variances, covariances and other moments in order to ensure that the results of any analyses, such as land use allocation in this study, are not affected by inherent scale mismatches. Here, these were observed under ES gradients aggregated to scales other than 100 and 500 m and can be determined by using local indicators of spatial association [34,35] or local spatial covariances [36].

3.
The impact of MAUP and aggregation scales should be evaluated alongside the scale of decision making. The support size and shape of the spatial units being used in spatial data analysis affect the patterns identified in the evaluations of ES and related concepts such as NC for a given spatial extent such as an agricultural field, a farm holding or river catchment. 4.
ES researchers and those in related disciplines (land use planning, landscape-scale decisions, etc.) should up-skill themselves in spatial analysis techniques. It is important that those undertaking research in these domains understand core paradigms associated with working with spatial data and understand techniques that are frequently used in spatial statistics. Scale blindness is commonly found in published ES research (as indicated above), where, for example, models constructed over one scale of spatial support are applied to data over another. Up-skilling is needed because powerful analytical tools that were previously the reserve of domain experts are now included in many off-the-shelf software environments and are easily applied in a naive manner. Such tools include those for spatial data aggregation (both up and down scaling), location allocation and spatial data integration. They will generate results without requiring the user to understand how to best parameterise them. Examples of similar misuse have been observed in the renewable energy literature with respect to land use [37].
The dangers of not undertaking such investigations into these MAUP and scale related considerations are erroneous inference and decision making. In the realm of ES and landscape decision making, there is a need to examine cross-scale sensitivities driven by the size and shape of areal units, as these relate to the scale component and aggregation or zoning component of the MAUP, respectively. Such investigations are needed to explicitly mitigate MAUP and to better handle different scales of decision making and response in models and decision tools. To perform this, there is a need to transfer and adapt concepts and methods from (i) Geostatistics [38,39], (ii) Spatial Ecology [35] and (iii) Quantitative Geography [40,41]. MAUP can be mitigated against by analysing data at the finest possible meaningful scale or unit [42]. Here, Geostatistics provides predictive tools to down-scale with an associated variance [43] and statistical tests of sensitivity relative to MAUP can inform on this ideal scale [44].
However, a 'solution' to the MAUP poses a very different challenge: spatial scales need to be identified where the study processes are considered stable with respect to their variances, covariances and higher moments in context of the intended data analysis. Such local scale stability has strong links to the notion of process non-stationarity, where investigations, for example, by using local indicators of spatial association [34,35] or local spatial covariances (variograms) [36] have the potential to provide 'solutions' to MAUP. Service, https://digimap.edina.ac.uk, Downloaded: 2019-04-26 15:16:52.69. We thank the anonymous reviewers of this manuscript whose comments and suggestions considerably improved the paper.