3. Results and Discussions of the Correlation Analysis between the Number and Power of HMKEs and the Settlements’ Level of Development
This research primarily sought to find answers to the questions whether the number and power of the HMKEs in a given settlement correlated with the settlement’s economic and infrastructural dimensions in Hungary, and if yes, to what extent.
The investigations first focused on the settlements that had HMKEs and where electricity was supplied by ELMŰ-ÉMÁSZ, EON, or NKM. Apart from the identification of the individual effects of the settlement indicators from the database containing the regions of all three electric companies and the demonstration of the relationships between the settlements’ ranking according to these and the ranking of the settlements based on the quantity of HMKEs, it was a main objective of this research to create a regression model that shows the relationship between the settlement indicators (
Table 1) and the quantity of total HMKEs per 1000 population at a national level. The resulting linear regression function can be seen here below (see Equation (3)):
where
x5: the number of electricity consumers per 1000 population, pcs;
x1: the number of registered economic organizations per 1000 population;
x3: the total budgetary expenditure of municipalities per 1000 population. It was established that both the regression model and the parameters were significant (
p = 0.000); furthermore, based on the value of are R (0.554), there was a moderately strong correlation between the explanatory variables of the model and the number of HMKEs per 1000 population, and the five indicators explained the differentiation of the quantity of HMKEs at the settlements’ level to an extent of 30.7%.
It was established that, concerning the settlements in the examined period, there was a positive weak moderate correlation between the following of the settlements’ indicators: registered businesses per 1000 population, total budgetary expenditure of the municipalities per 1000 population, number of operating commercial accommodation units per 1000 population, number of businesses in the hospitality industry per 1000 population, and the number of total HMKEs per 1000 population. A somewhat stronger but still moderate correlation could be detected between the number of residential electricity consumers per 1000 population, the number of electricity consumers per 1000 population, and the quantity of total HMKEs per 1000 population (
Table 2). The partial correlation coefficients showed that, although in most of the cases the controlled variables had some effect on the strength of the relationship, this effect did not significantly modify the correlation.
The ranking of the settlements based on the settlements’ complex indicators and the ranking according to the number of HMKEs per 1000 population indicated a positive weak moderate correlation (ρ = 0.226, p = 0.000).
Next, those 2417 settlements were analyzed where ELMŰ-ÉMÁSZ and EON are the providers of electricity. Regarding these settlements, it was possible to examine the power of the HMKEs apart from their numbers with regard to both the residential and the business consumers.
It was established that concerning the settlements in the examined period, there was a positive weak moderate correlation between the following of the settlements’ indicators: the number of registered businesses per 1000 population, the total budgetary revenue of the municipalities per 1000 population, the total budgetary expenditure of the municipalities per 1000 population, the number of businesses in the hospitality industry per 1000 population, and the power and the number of total HMKEs per 1000 population. The relationship between the number of total HMKEs per 1000 population and the number of units of operating commercial accommodation units per 1000 population was also a weak moderate positive one. Between the number of residential electricity consumers per 1000 population, the number of electricity consumers per 1000 population, the length of the low-voltage electricity distribution network per 1000 population, and the total power of HMKEs per 1000 population there was also a weak moderate correlation. Between the number of residential electricity consumers per 1000 population, the number of electricity consumers per 1000 population, the length of the low-voltage electricity distribution network per 1000 population, and the quantity of total HMKEs per 1000 population there was a somewhat stronger but still moderate correlation (
Table 3 and
Table 4). The partial correlation coefficients showed that, although in most of the cases the controlled variables had some effect on the strength of the relationship, this effect did not significantly modify the correlation.
The ranking of the settlements based on the settlements’ complex indicators and the ranking according to the number * and power ** of HMKEs per 1000 population indicated a positive weak moderate correlation (* ρ = 0.298 p = 0.000; ** ρ = 0.266 p = 0.000).
There was a positive weak moderate correlation between the number of registered businesses per 1000 population, the number of businesses in the hospitality industry per 1000 population, and the power and number of residential HMKEs per 1000 population. The relationship between the number of residential HMKEs per 1000 population and the number of units of operating commercial accommodation units per 1000 population was also a weak moderate one. There was a stronger but still moderate positive correlation between the quantity and power of residential HMKEs per 1000 population and the number of residential electricity consumers per 1000 population, the number of electricity consumers per 1000 population, and the length of the low-voltage electricity distribution network per 1000 population (
Appendix A Table A1 and
Table A2). The partial correlation coefficients showed that, although in most of the cases the controlled variables had some effect on the strength of the relationship, this effect did not significantly modify the correlation.
The ranking of the settlements based on the settlements’ complex indicators and the ranking according to the number * and power ** of residential HMKEs per 1000 population indicated a loose positive correlation (* ρ = 0.188 p = 0.000; ** ρ = 0.155 p = 0.000).
It was found that in the examined period, there was a positive weak moderate correlation between the number of registered businesses per 1000 population, the total budgetary revenue of the municipalities per 1000 population, the total budgetary expenditure of the municipalities per 1000 population and the quantity, and the power of business HMKEs per 1000 population. In addition, there was a weak moderate positive correlation between the quantity of business HMKEs per 1000 population and the number of electricity consumers per 1000 population and the length of the low-voltage electricity distribution network per 1000 population (
Appendix A Table A3 and
Table A4). The partial correlation coefficients showed that, although in most of the cases the controlled variables had some effect on the strength of the relationship, this effect did not significantly modify the correlation.
The ranking of the settlements based on the settlements’ complex indicators and the ranking according to the number * and power ** of business HMKEs per 1000 population indicated a positive weak moderate correlation (* ρ = 0.203 p = 0.000; ** ρ = 0.193 p = 0.000).
For more detailed research results, analyses were carried out regarding the 1763 settlements where EON is the electricity supplier, examining the HMKE quantity and power per 1000 population data in total but also at residential, business, and public levels.
It was established that concerning the settlements in the examined period, there was a positive weak moderate correlation between the number of registered businesses per 1000 population, the length of the low-voltage electricity distribution network per 1000 population, the number of businesses in the hospitality industry per 1000 population, and the power and number of all HMKEs per 1000 population. Relationships of the same direction and similar strength were detected between the number of operating commercial accommodation units per 1000 population and the number of total HMKEs per 1000 population; and the total budgetary expenditure of the municipalities per 1000 population, the number of residential electricity consumers per 1000 population, and the power of total HMKEs per 1000 population. A still moderate but somewhat stronger correlation was found between the number of residential electricity consumers per 1000 population, the number of electricity consumers per 1000 population, and the quantity of total HMKEs per 1000 population (
Table 5 and
Table 6). The partial correlation coefficients showed that, although in most of the cases the controlled variables had some effect on the strength of the relationship, this effect did not significantly modify the correlation.
The ranking of the settlements based on the settlements’ complex indicators, which were created based on the settlements’ indicators, and the ranking according to the number * and power ** of total HMKEs per 1000 population indicated a positive weak moderate correlation (* ρ = 0.208 p = 0.000; ** ρ = 0.244 p = 0.000).
There was a positive weak moderately close correlation between the number of registered businesses per 1000 population, the length of the low-voltage electricity distribution network per 1000 population, the number of businesses in the hospitality industry per 1000 population, and the quantity and power of residential HMKEs per 1000 population. The relationship between the number of residential HMKEs per 1000 population and the number of units of operating commercial accommodation units per 1000 population was also a positive weak moderate one. A still moderate but somewhat stronger correlation was observed between the number of residential electricity consumers per 1000 population, the number of electricity consumers per 1000 population, and the quantity and power of residential HMKEs per 1000 population (
Appendix A Table A5 and
Table A6). The partial correlation coefficients showed that, although in most of the cases the controlled variables had some effect on the strength of the relationship, this effect did not significantly modify the correlation.
The ranking of the settlements based on the settlements’ complex indicators and the ranking according to the number * and power ** of residential HMKEs per 1000 population indicated a loose positive correlation (* ρ = 0.133 p = 0.000; ** ρ = 0.117 p = 0.000).
It was found that in the examined period, there was a positive weak moderate correlation between the total budgetary revenue of the municipalities per 1000 population, the total budgetary expenditure of the municipalities per 1000 population, and the quantity and power of public HMKEs per 1000 population (
Appendix A Table A7 and
Table A8). The partial correlation coefficients showed that, although in most of the cases the controlled variables had some effect on the strength of the relationship, this effect did not significantly modify the correlation.
The ranking of the settlements based on the settlements’ complex indicators and the ranking according to the number * and power ** of public HMKEs per 1000 population indicated a positive weak correlation (* ρ = 0.103 p = 0.000; ** ρ = 0.139 p = 0.000).
In the examined period, there was a positive weak moderate relationship between the number of registered businesses per 1000 population, the number of units of operating commercial accommodation units per 1000 population, and the quantity of business HMKEs per 1000 population. The power of business HMKEs per 1000 population did not show any close correlation with any of the settlement indicators. However, there was a positive but weak correlation between the number of registered businesses per 1000 population, the total budgetary revenue of the municipalities per 1000 population, the total budgetary expenditure of the municipalities per 1000 population, the number of residential electricity consumers per 1000 population, the number of electricity consumers per 1000 population, the length of the low-voltage electricity distribution network per 1000 population, the number of operating units of commercial accommodation units per 1000 population, the number of businesses in the hospitality industry per 1000 population, and the power of business HMKEs per 1000 population (
Appendix A Table A9 and
Table A10). The partial correlation coefficients showed that, although in most of the cases the controlled variables had some effect on the strength of the relationship, this effect did not significantly modify the correlation.
The ranking of the settlements based on the settlements’ complex indicators, which were created on the basis of the settlement indicators, and the ranking according to the number * and power ** of business HMKEs indicated a positive weak correlation (* ρ = 0.128 p = 0.000; ** ρ = 0.141 p = 0.000).
At the beginning of the study, three hypotheses were formulated, and they were confirmed, as shown below. The following points were proven:
- -
There are certain settlement indicators each of which individually shows a correlation with the quantity and power of photovoltaic HMKEs. These relationships could be identified irrespective of the service regions of the specific electricity suppliers. There was a moderately strong correlation between the number of registered economic organizations per 1000 population, the number of operating commercial accommodation units per 1000 population, the number of businesses in the hospitality industry per 1000 population, and the quantity of total photovoltaic HMKEs per 1000 population. Furthermore, there was also a moderately strong correlation between the total budgetary expenditure of the municipalities per 1000 population, the number of businesses in the hospitality industry per 1000 population, the number of residential electricity consumers per 1000 population, the number of electricity consumers per 1000 population, the length of the low-voltage electricity distribution network per 1000 population, and the power of total photovoltaic HMKEs per 1000 population.
- -
The ranking of the settlements based on the complex indicator created from the settlement indicators showed a correlation with the ranking of the settlements according to the number and power of the photovoltaic HMKEs per 1000 population. The relationship between the rankings was moderately strong, with the exception of the residential, public, and business photovoltaic HMKE users in the service region of EON, where the correlation was only weak.
- -
A regression model was created from the settlements’ database containing data from all three electricity supplier regions. In this model, the quantity of photovoltaic HMKEs per 1000 population is explained by the number of electricity consumers per 1000 population, the number of economic organizations per 1000 population, and the total budgetary expenditure of the municipalities per 1000 population.
4. Conclusions
Based on the results of this study, it was established that according to the analyses from all three examination aspects (settlements with ELMŰ-ÉMÁSZ, EON, NKM or ELMŰ-ÉMÁSZ, EON, or only EON as the suppliers of electricity) there was a positive weak moderate correlation between the settlements’ indicators of the number of registered businesses per 1000 population, the number of operating commercial accommodation units per 1000 population, the number of businesses in the hospitality industry per 1000 population, and the number of total photovoltaic HMKEs per 1000 population. Between the number of residential electricity consumers per 1000 population, the number of electricity consumers per 1000 population, and the quantity of total photovoltaic HMKEs, there was a somewhat stronger but still moderate correlation regardless whether the electricity supply regions of ELMŰ-ÉMÁSZ and EON or only those of EON were considered. Additionally, in these two types of electricity supply regions (ELMŰ-ÉMÁSZ and EON or only EON), there was a weak moderate correlation between the total budgetary expenditure of the municipalities per 1000 population, the number of businesses in the hospitality industry per 1000 population, the number of residential electricity consumers per 1000 population, the number of electricity consumers per 1000 population, the length of the low-voltage electricity distribution network per 1000 population, and the total power of photovoltaic HMKEs per 1000 population.
The investigations also highlighted that in the regions where ELMŰ-ÉMÁSZ and EON or only EON were the suppliers of electricity, there was a positive weak moderate correlation between the number of registered businesses per 1000 population, the number of businesses in the hospitality industry per 1000 population, the number of operating commercial accommodation units per 1000 population, and the quantity of residential photovoltaic HMKEs per 1000 population. The strength of the correlation between the number of registered businesses per 1000 population, the number of businesses in the hospitality industry per 1000 population, and the power of residential photovoltaic HMKEs per 1000 population was also positive weak moderate. There was a stronger but still moderate positive correlation between the quantity and power of residential photovoltaic HMKEs per 1000 population and the number of residential electricity consumers per 1000 population and the number of electricity consumers per 1000 population.
According to our results, the business photovoltaic HMKEs per 1000 population showed a positive weak moderate relationship with the number of registered businesses per 1000 population regardless of the regions of electricity supply. Furthermore, it was found that in the region of the EON electric company, there was also a positive weak moderate correlation between the total budgetary revenue of the municipalities per 1000 population, the total budgetary expenditure of the municipalities per 1000 population, and the quantity and power of public photovoltaic HMKEs per 1000 population.
It was established that the rankings of the settlements based on the settlements’ complex indicators, which were created based on the settlement indicators, and the rankings according to the number and power of (total, residential, business) photovoltaic HMKEs per 1000 population indicated a positive loose correlation regardless of the electric companies’ regions.
Having analyzed the settlements of the three electricity companies’ service regions, it can be stated that it is possible to describe the quantity of photovoltaic HMKEs per 1000 population with a regression model, in which the following settlement indicators can be regarded as explanatory factors: the number of electricity consumers per 1000 population, the number of registered economic organizations per 1000 population, and the total budgetary expenditure of the municipalities per 1000 population.
The usefulness of the results of this study is diverse. On the one hand, it may be of help with the planning of energy policy and energy strategy in terms of identifying the factors playing a part in the spread of photovoltaic HMKEs, while, on the other hand, it can inspire scientists to continue the research in this field. The objectives of such future investigations will involve the examination of other regional levels (NUTS 3, LAU 1), and processing data from 2020 will also allow the observation of changes over time.