Quantifying the Asymmetric Socioeconomic Burden of Residential Electricity Tariffs: The Energy-Economic Impact Index Framework

Abstract

Residential electricity tariff structures in emerging economies are often highly complex, dynamically combining regional climatic variables and multi-tiered price adjustments. In Mexico, despite a preferential scheme designed to mitigate seasonal expenditure fluctuations, baseline energy subsidies frequently fail to protect low-income households due to structural targeting inefficiencies based strictly on regional temperature thresholds rather than socioeconomic status. This study addresses this methodological and regulatory gap by developing the Energy-Economic Impact Index (EEII), a novel mathematical and heuristic framework that couples complex Increasing Block Tariffs (IBT) architecture with localized household income dynamics at the state level. The proposed methodology was comprehensively validated using synchronized biennial empirical datasets from all 32 Mexican states, combining Federal Electricity Commission (CFE) billing data and National Surveys of Household Income and Expenditures (ENIGH) spanning the 2018–2024 period. Quantitative results reveal a severe, non-linear escalation of the financial energy burden across the territory, demonstrating that rising residential electricity costs significantly outpaced domestic income growth trajectories. Notably, households situated within high-temperature geographic regimes (Tariffs 1D, 1E, and 1F) exhibited the most critical economic vulnerability, with the calculated energy cost impact absorbing up to 14.90% of the real monthly household income in 2024. Ultimately, the EEII framework proves to be a robust predictive and decision-support tool for energy policy planners aiming to optimize fiscal subsidy allocation, reduce energy poverty gaps, and mitigate socioeconomic risks in transition economies.

1. Introduction

The literature frequently cites Mexico’s electricity tariff structure as one of the most complex in Latin America [1]. Residential tariffs are no exception to this complexity, as they dynamically combine regional climatic variables, increasing blocks of electricity consumption, and government subsidies based on geographical location [2,3]. Consequently, residential service is categorized into seven distinct tariff brackets across the Mexican territory: 1, 1A, 1B, 1C, 1D, 1E, and 1F (Figure 1) [4]. The threshold for each category is determined by the minimum average temperature recorded during the summer season within each specific region, resulting in the significant spatial diversity illustrated in Figure 1 [5]. Within this context, Mexico exhibits profound climatic diversity across its territory throughout the year due to its geographical configuration, which encompasses arid, tropical, temperate, and mountainous regions. This variation gives rise to a wide range of annual average temperatures, as depicted in Figure 2 [6]. This extensive thermal gradient directly influences household electricity consumption patterns, particularly in warm regions where the deployment of cooling systems significantly intensifies energy demand [7,8]. Empirical studies on residential demand have demonstrated that households located in high-temperature zones exhibit electricity consumption levels well above the national average, especially those utilizing air conditioning systems [9]. Consequently, substantial variability exists within the electricity consumption patterns of Mexican households. This variability is directly proportional to economic vulnerability, which impacts families asymmetrically depending on their income levels. For instance, during peak summer seasons in warmer regions, household electricity consumption escalates into higher consumption blocks established by the Federal Electricity Commission (Comisión Federal de Electricidad, CFE). In these upper blocks, the marginal cost per kilowatt-hour (kWh) increases substantially as government subsidies are progressively phased out [10]. To mitigate these fluctuations, the Mexican government implements a preferential tariff scheme based on seasonal summer adjustments and annual maximum average temperatures. Nevertheless, this increasing block tariff (IBT) architecture has generated intensive debate in the specialized literature regarding market incentives and budgetary inefficiencies [11,12]. While the baseline consumption blocks (basic and intermediate) receive substantial subsidies, the rapid loss of this benefit severely penalizes low-income families who lack access to energy-efficient technologies or optimal thermal insulation in their dwellings [13,14]. Furthermore, recent economic analyses indicate that the currently applied electricity pricing model suffers from significant targeting deficiencies, as subsidy allocation is fundamentally linked to regional temperatures rather than the socioeconomic status of the end-users. Consequently, higher-income segments of the population—who possess a greater capacity for electrical appliance acquisition—absorb a disproportionate share of the public resources allocated to alleviate electricity expenditures [15,16]. This structural imbalance not only constrains the state’s fiscal capacity due to the magnitude of the required public subsidies but also hinders the transition toward clean energy sources and residential photovoltaic microgeneration, while simultaneously widening structural equity gaps across the country’s diverse regions [17,18]. Therefore, accurately assessing the financial burden of electricity on Mexican households requires a comprehensive analytical framework that couples electricity expenditure realities with the localized dynamics of income inequality at the state level [19,20]. Recent literature addressing residential electricity sectors in emerging economies can be broadly categorized into three domain streams: (i) econometric evaluations of residential demand under Increasing Block Tariff (IBT) mechanisms [2,10]; (ii) energy poverty and affordability frameworks [21,22], and (iii) spatial analyses of climate-driven energy vulnerability [6,8]. Despite these contributions, significant methodological gaps remain. Most econometric models simplify tariff structures into average prices, failing to capture the non-linear cost jumps occurring when household consumption crosses volumetric block thresholds ($L_B$ and $L_I$). Conversely, traditional energy poverty frameworks rely on static, cross-sectional income metrics that fail to track compound price escalations ($CAG{R}_{EB}$) over longitudinal horizons. Consequently, there is a critical lack of integrated models capable of coupling piecewise tariff algorithms with localized household income dynamics across shifting climatic regimes. Based on the aforementioned factors, a complex intersection emerges among geography, climate, electricity pricing regulations, and household economics, highlighting the critical need to develop robust metric tools. Conventional indicators frequently fail to capture the dynamic impact of electricity costs on household income, primarily because they neglect temporal variations and the specific socio-economic conditions of each Mexican state. The proposed Energy-Economic Impact Index (EEII), tailored specifically to residential tariffs, addresses this methodological gap [21]. This index establishes a heuristic framework that systematically quantifies the proportion of real monthly household income absorbed by electricity expenditures [23,24]. By integrating empirical data from consumption records and institutional income surveys, it becomes possible to precisely identify the population segments under the greatest economic distress [25,26] relative to their assigned tariff categories. Consequently, the EEII serves as an essential scientific instrument to evaluate current energy policies and to guide the design of a more equitable, efficient, and progressive tariff framework in Mexico. Conventional static indicators frequently fail to capture the dynamic impact of electricity costs on household income because they neglect temporal variations, compound price escalations, and the multi-tiered structure of Increasing Block Tariffs (IBT). To address these limitations, the proposed Energy-Economic Impact Index (EEII) incorporates a dynamic compound annual growth rate ($CAG{R}_{EB}$) that captures longitudinal socio-energetic divergence under shifting regional temperature regimes. Table 1 provides a comparative synthesis contrasting traditional energy burden metrics against the proposed EEII framework. To bridge these literature gaps, this study provides three key scientific contributions:

Figure 1. Geographic distribution of residential electricity tariffs in Mexico and their correlation with regional temperature thresholds [3].

Figure 2. Spatial distribution of extreme maximum temperatures across Mexico, illustrating regional thermal gradients [5].

Table 1. Comparative Summary of Energy Burden Indicators and Methodological Scope.

Indicator/ Metric	Mathematical Formulation/ Basis	Consideration of IBT Structures	Longitudinal Compound Modeling (CAGR)	Sensitivity to Regional Climate Gradients	Primary Limitations
Ten Percent Rule (10%)	${\displaystyle \frac{\mathrm{Energy} \mathrm{Expenditure}}{\mathrm{Income}}}>10\%$	No	No	No	Fixed arbitrary threshold; ignores local income dynamics and tariff block jumps.
Low Income High Costs (LIHC)	Dual threshold (Income < Poverty line & Energy Costs > Median)	No	No	Indirect	Complex data requirements; static snapshot; fails to model dynamic block rates.
Static Energy Burden ($\mathit{E}\mathit{I}\mathit{P}$)	$EIP={\displaystyle \frac{\mathrm{Energy} \mathrm{Expenditure}}{\mathrm{Monthly} \mathrm{Income}}}\times 100\%$	Partial	No	Partial	Captures immediate local financial stress but misses compounding annual tariff updates.
Proposed EEII Framework	$EEI{I}_t=EI{P}_{t_0}·{\left(1+CAG{R}_{EB}\right)}^{\left(t-t_0\right)}$	Yes (Full Piecewise Modeling)	Yes ($CAG{R}_{EB}$ Integration)	Yes (Coupled with Tariff-Temp Regimes)	Requires synchronized multi-period billing and income survey datasets.

Formulation of a Dynamic Metric: We develop the Energy-Economic Impact Index (EEII), coupling piecewise Increasing Block Tariff (IBT) functions with compound annual growth rate ($CAG{R}_{EB}$) metrics to eliminate static tracking biases.

Empirical Multi-Period Synchronization: We construct a synchronized 2018–2024 socio-energetic database integrating CFE billing parameters and ENIGH real income datasets across all 32 Mexican federal states.

Policy-Oriented Spatial Mapping: We introduce a multi-dimensional Socio-Energetic Space map and density matrix to locate regressive subsidy targeting and support adaptive tariff modernization in transition economies.

2. Theoretical Framework and Econometric-Energy Modeling

2.1. Mathematical Structure and Subsidies of the Increasing Block Tariff (IBT) System

Residential electricity pricing in emerging economies often utilizes structured regulatory frameworks to balance utility cost recovery with social welfare objectives [27]. In the Mexican power sector, the state utility operates under an Increasing Block Tariff (IBT) framework, where the unit price of energy increases stepwise as consumption crosses specific volumetric thresholds [28], where exceeding the designated baseline threshold (the High Consumption Limit, DAC) results in the complete removal of government subsidies. To formalize this structure, let EC_T represent the total monthly electricity consumption of a household (expressed in kWh). Under the IBT architecture, this volume is non-linearly segmented into three distinct operational blocks: basic consumption (EC_B), intermediate consumption (EC_I), and surplus consumption (EC_S). The segment volumes are mathematically constrained by the upper limits of the basic block (L_B) and the intermediate block (L_I) as follows:

$${EC}_B=\mathrm{m}\mathrm{i}\mathrm{n}({EC}_T,L_B)$$

(1)

$${EC}_I=max({0,\mathrm{m}\mathrm{i}\mathrm{n}(EC}_T-L_B,L_I))$$

(2)

$${EC}_S=max(0,{EC}_T-\left(L_B+L_I\right))$$

(3)

Consequently, the total monthly cost of electricity consumption ($CC{E}_m$, expressed in MXN) is determined by mapping each consumption block to its corresponding heavily subsidized or non-subsidized unit price rate ($P_B$, $P_I$, and $P_S$), defined by:

$${CCE}_m={(EC}_B·P_B)+{(EC}_I·P_I)+{(EC}_S·P_S)$$

(4)

where

$E{C}_T$: Total monthly household electricity consumption ($\mathrm{kWh}$).

$E{C}_B$: Electricity volume allocated to the baseline basic block ($\mathrm{kWh}$).

$E{C}_I$: Electricity volume allocated to the intermediate block ($\mathrm{kWh}$).

$E{C}_S$: Electricity volume designated as surplus or high-demand consumption ($\mathrm{kWh}$).

$L_B$: Volumetric upper limit parameter for the basic consumption tier ($\mathrm{kWh}$).

$L_I$: Volumetric upper limit parameter for the intermediate consumption tier ($\mathrm{kWh}$).

$CC{E}_m$: Total calculated monthly cost of electricity billing ($\mathrm{MXN}$).

$P_B$: Unit pricing rate assigned to the basic block ($\mathrm{MXN}/\mathrm{kWh}$).

$P_I$: Unit pricing rate assigned to the intermediate block ($\mathrm{MXN}/\mathrm{kWh}$).

$P_S$: Unit pricing rate assigned to the surplus block ($\mathrm{MXN}/\mathrm{kWh}$).

The structural variability of these operational parameters ($L_B$, $L_I$, $P_B$, $P_I$, and $P_S$) across the different regions of the country is comprehensively detailed in Table 2. As observed, the volumetric thresholds for subsidy eligibility dynamically expand as a function of the minimum average summer temperature recorded in each specific Mexican state, moving from the restrictive thresholds of Tariff 1 to the extended baselines of Tariff 1F.

Table 2. Residential Electricity Tariff Classification in Mexico.

Tariff	Average Minimum Temperature (Summer)	Subsidy Ceiling/High Consumption Limit (DAC)	Location	Tariff
				Basic	Intermediate	Surplus
1	<25 °C	≥140 kWh/month	Aguascalientes, Baja California, Chihuahua, Chiapas, Coahuila, Durango, City of México, Guanajuato, Guerrero, Hidalgo, Jalisco, México, Michoacán, Nuevo León, Oaxaca, Puebla, Querétaro, Tlaxcala, San Luis Potosi, Veracruz, Zacatecas	From 0 to 75 kWh/month	next 65 kWh/month (after the first 75 kWh)	Additional kWh/month (after the two kWh amounts)
1A	25 °C	≥150 kWh/month	Baja California Sur, City of México, Chiapas, Chihuahua, Coahuila, Colima, Durango, Guerrero, Jalisco, Michoacán, Morelos, Nayarit, Oaxaca, Puebla, Querétaro, San Luis Potosi, Sonora, Tabasco, Tamaulipas, Veracruz, Yucatán.	From 0 to 75 kWh/month	next 75 kWh/month (after the first 75 kWh)	Additional kWh/month (after the two kWh amounts)
1B	28 °C	≥225 kWh/month	Baja California, Chiapas, Chihuahua, Coahuila, Colima, Durango, Guerrero, Hidalgo, Jalisco, Michoacán, Nayarit, Oaxaca, Puebla, Quintana Roo, San Luis Potosi, Sonora, Tabasco, Tamaulipas, Veracruz, Yucatán	From 0 to 75 kWh/month	next 100 kWh/month (after the first 75 kWh)	Additional kWh/month (after the two kWh amounts)
1C	30 °C	≥500 kWh/month	Baja California Sur, Campeche, Chiapas, Chihuahua, Coahuila, Durango, Guerrero, Hidalgo, Michoacán, Nuevo León, Oaxaca, Quintana Roo, Sonora, Tabasco, Tamaulipas, Veracruz, Yucatán	From 0 to 75 kWh/month	next 100 kWh/month (after the first 75 kWh)	Additional kWh/month (after the two kWh amounts)
1D	31 °C	≥600 kWh/month	Baja California Sur, Chihuahua, Coahuila, Durango, Guerrero, Michoacán, Nuevo León, Quintana Roo, San Luis Potosi, Sinaloa, Sonora, Tamaulipas, Veracruz	From 0 to 75 kWh/month	next 125 kWh/month (after the first 75 kWh)	Additional kWh/month (after the two kWh amounts)
1E	32 °C	≥900 kWh/month	Baja California Sur, Coahuila, Guerrero, Nayarit, Nuevo León, Sinaloa, Sonora, Tamaulipas	From 0 to 75 kWh/month	next 125 kWh/month (after the first 75 kWh)	Additional kWh/month (after the two kWh amounts)
1F	≥33 °C	≥2500 kWh/month	Baja California, Sinaloa, Sonora	From 0 to 75 kWh/month	next 125 kWh/month (after the first 75 kWh)	Additional kWh/month (after the two kWh amounts)

* Note: CFE defines the summer temperature baseline as the average minimum temperature recorded during the 6 consecutive warmest months of the year, verified over a 3-year historical window within the last 5 years.

The spatial unit for tariff allocation is defined at the municipal level based on local thermal records from CONAGUA/SMN stations [29] rather than uniform state boundaries, explaining why states like Coahuila exhibit multiple coexisting tariffs (see Table 3). Governed by DOF agreements, allocation follows an ascending rule: Tariff 1 acts as the baseline ($<25 °\mathrm{C}$), while Tariffs 1A to 1F are progressively assigned as the local 3-year average minimum summer temperature reaches $25 °\mathrm{C},28 °\mathrm{C},30 °\mathrm{C},31 °\mathrm{C},32 °\mathrm{C}$, and $\ge 33 °\mathrm{C}$, respectively.

Table 3. Real Annual Household Income Baseline by State in Mexico (2018–2024) [24] and its Percentage Variance.

Mexican State	Real Annual Income (MXN)				Variation (%)
	2018	2020	2022	2024	2018 vs. 2024
Aguascalientes	80,139	73,475	86,517	89,819	12.08
Baja California	79,916	85,468	98,258	101,187	26.62
Baja California Sur	92,881	80,989	101,026	104,728	12.75
Campeche	64,413	59,578	63,498	66,236	2.83
Chiapas	35,799	36,757	44,033	41,084	14.76
Chihuahua	72,958	75,945	90,541	92,363	26.60
City of México	106,797	84,884	98,698	110,685	3.64
Coahuila	75,521	70,157	83,023	87,652	16.06
Colima	71,240	70,947	76,607	86,241	21.06
Durango	58,940	63,466	63,234	69,589	18.07
Guanajuato	62,310	60,979	66,417	74,545	19.64
Guerrero	39,614	40,977	46,143	48,548	22.55
Hidalgo	52,373	50,522	58,830	59,461	13.53
Jalisco	81,750	70,252	79,286	87,199	6.67
México	64,836	62,532	63,248	74,162	14.38
Michoacán	57,597	58,487	62,946	68,234	18.47
Morelos	58,032	52,981	63,255	66,195	14.07
Nayarit	65,020	65,487	72,192	74,601	14.74
Nuevo León	93,121	91,908	95,060	117,034	25.68
Oaxaca	42,661	45,699	47,899	52,025	21.95
Puebla	52,632	49,925	54,574	62,162	18.11
Querétaro	82,830	76,161	82,834	97,615	17.85
Quintana Roo	76,580	58,448	79,460	88,754	15.90
San Luis Potosí	62,787	60,263	66,466	73,205	16.59
Sinaloa	74,908	70,363	79,445	82,837	10.59
Sonora	80,867	77,324	83,182	94,721	17.13
Tabasco	53,273	52,507	57,571	65,001	22.01
Tamaulipas	66,374	62,618	70,198	77,302	16.46
Tlaxcala	54,423	47,786	51,161	58,834	8.10
Veracruz	43,813	44,267	49,329	53,030	21.04
Yucatán	67,355	58,935	68,927	79,972	18.73
Zacatecas	51,261	55,960	55,405	60,292	17.62
National Average	67,319	63,400	70,391	77,864

2.2. Mathematical Formulation and Non-Linear Compound Growth of the EEII

To evaluate how these multi-tiered tariff structures stress household finances, it is necessary to establish a static socio-energetic baseline metric. The Economic Impact Percentage ($EI{P}_{s,m}$) for a specific federal state $s$ during a designated month $m$ is defined as the ratio between the household electricity expenditure ($HE{E}_{s,m}$) and the monthly household income ($MH{I}_{s,m}$), formalized as follows:

$$EI{P}_{s,m}=\left({\displaystyle \frac{HE{E}_{s,m}}{MH{I}_{s,m}}}\right)·100\%$$

(5)

where

$EI{P}_{s,m}$: Static Economic Impact Percentage representing the immediate energy budget burden ($\%$).

$HE{E}_{s,m}$: Average household electricity expenditure within state $s$ during month $m$ ($\mathrm{MXN}$).

$MH{I}_{s,m}$: Average Monthly Household Income compiled for state $s$ during month $m$ ($\mathrm{MXN}$).

While the $EI{P}_{s,m}$ captures localized, cross-sectional financial stress, it fails to account for longitudinal macroeconomic variances, shifting regional climate regimes, and compound annual tariff escalations [22]. To overcome this linear bias, the temporal trajectory of the annual average impacts ($\overline{EIP}$) must be analyzed dynamically.

Let ${\overline{EIP}}_{t_0}$ and ${\overline{EIP}}_{t_f}$ represent the annualized socioeconomic energy burden baseline at the initial period ($t_0=2018$) and the final period ($t_f=2024$), respectively. The Compound Annual Growth Rate of the energy burden ($CAG{R}_{EB}$) over an analytical horizon of $n$ years (where $n=t_f-t_0$) is mathematically defined as:

$$CAG{R}_{EB}={\left({\displaystyle \frac{{\overline{EIP}}_{t_f}}{{\overline{EIP}}_{t_0}}}\right)}^{{\displaystyle \frac{1}{n}}}-1$$

(6)

where:

$CAG{R}_{EB}$: Compound Annual Growth Rate of the household energy financial burden ($\%$ as a decimal).

${\overline{EIP}}_{t_0}$: Annual average baseline economic impact at the starting multi-period checkpoint ($t_0$).

${\overline{EIP}}_{t_f}$: Annual average final economic impact at the terminal longitudinal checkpoint ($t_f$).

$n$: Total chronological duration of the evaluated timeframe ($n=6$ years for the 2018–2024 scope).

The calculated $CAG{R}_{EB}$ represents a non-linear compounding structural factor that reflects the systemic divergence between state utility price indexes and national household income trajectories. Ultimately, the dynamic Energy-Economic Impact Index ($EEI{I}_t$) for any intermediate or prospective year $t$ (where $t>t_0$) is formulated as a function of the compounding expansion of historical socio-energetic baselines:

$$EEI{I}_t=EI{P}_{t-1}·\left(1+CAG{R}_{EB}\right)$$

(7)

Alternatively, the index can be generalized directly from the absolute historical baseline ($t_0$) utilizing the following multi-tiered exponential expression:

$$EEI{I}_t=EI{P}_{t_0}·{\left(1+CAG{R}_{EB}\right)}^{\left(t-t_0\right)}$$

(8)

where:

$EEI{I}_t$: The dynamic Energy-Economic Impact Index projected for period $t$ (Dimensionless/Index unit).

$EI{P}_{t-1}$: The calculated socioeconomic energy burden from the immediate preceding year ($\%$).

$EI{P}_{t_0}$: The empirical baseline economic impact percentage calculated at the initial year ($\%$).

This integrated econometric-energy formulation eliminates tracking anomalies caused by flat linear projections, ensuring that the dynamic pressure exerted by climate fluctuations and block tariff structures on residential economics is rigorously captured for policy design [30].

3. Results and Empirical Validation

3.1. Data Synchronization and Socio-Energetic Baselines

The experimental verification of the econometric-energy modeling framework required a rigorous multi-period data synchronization. Given that the residential electricity parameters published by the CFE operate under annualized timelines, the socioeconomic datasets derived from the National Survey of Household Income and Expenditures ($ENIGH$) were structured under its specific biennial execution frequency. Consequently, the comparative horizon was consolidated into a longitudinal framework incorporating tracking checkpoints for the years 2018, 2020, 2022, and 2024. In this framework, household parameters represent state-level average representative domestic units without explicitly stratifying by individual occupancy counts.

Table 3 presents the calibrated real annual household income (adjusted for inflation using the Consumer Price Index [INPC] and expressed in constant MXN) across the 32 Mexican states, establishing the baseline macroeconomic diversity under the assumption of stable intra-annual distribution behaviors. The synchronized empirical datasets demonstrate high cross-sectional inequality; for instance, the terminal year 2024 exhibits a severe structural gap between the lowest regional income capacity recorded in Chiapas ($$\mathrm{41,084}$ MXN/month) and the highest baseline observed in Nuevo León ($$\mathrm{117,034}$ MXN/month).

Concurrently, the absolute annual electricity consumption aggregates were mapped across their corresponding territorial jurisdictions to extract the real household energy inputs ($E{C}_T$, expressed in $\mathrm{kWh}/\mathrm{month}$), as consolidated in Table 4. The intersection of these variables confirms that macro-climatic regimes act as a primary forcing parameter, yielding a distinct consumption expansion in high-temperature zones where space cooling is mandatory.

Table 4. Total Annual Residential Electricity Consumption by Mexican State (kWh/year).

Mexican State	Year (kWh)				Tariff
	2018	2020	2022	2024	1	1A	1B	1C	1D	1E	1F
Aguascalientes	1363	1334	1387	1416	*
Baja California	3370	3740	3875	4138	*		*				*
Baja California Sur	3456	3972	3466	3699		*		*	*	*
Campeche	2748	2948	3119	3275				*
Chiapas	1270	1288	1271	1267	*	*	*	*
Chihuahua	1923	2114	2149	2251	*	*	*	*	*
City of México	1164	1247	1336	1429	*	*
Coahuila	2376	2456	2588	2717	*	*	*	*	*	*
Colima	2076	2231	2239	2307		*	*
Durango	1281	1354	1392	1449	*	*	*	*	*
Guanajuato	936	983	1007	1041	*
Guerrero	1338	1341	1425	1485	*	*	*	*	*	*
Hidalgo	1464	1632	1726	1860	*		*	*
Jalisco	921	1016	1026	1071	*	*	*
México	1383	1485	1522	1587	*
Michoacán	1389	1506	1544	1617	*	*	*	*	*
Morelos	1539	1619	1742	1860		*
Nayarit	1957	2140	2207	2326		*	*			*
Nuevo León	3000	2985	3053	3096	*			*	*	*
Oaxaca	1169	1281	1303	1365	*	*	*	*
Puebla	1155	1186	1272	1344	*	*	*
Querétaro	1323	1375	1457	1535	*	*
Quintana Roo	2986	3288	3646	4019			*	*	*
San Luis Potosí	1537	1679	1801	1943	*	*	*		*
Sinaloa	4588	5141	5182	5432					*	*	*
Sonora	4854	5692	5790	6194		*	*	*	*	*	*
Tabasco	2890	3235	3007	2991		*	*	*
Tamaulipas	2870	3208	3174	3286		*	*	*	*	*
Tlaxcala	1111	1170	1217	1274	*
Veracruz	1659	1741	1776	1834	*	*	*	*	*
Yucatán	2458	2735	2814	2981		*	*	*
Zacatecas	1259	1272	1344	1401	*

3.2. Piecewise Cost Disaggregation and Cross-Sectional EIP Trajectories

Utilizing the pricing matrices established for the increasing block tariff tiers (Table 5), the monthly household electricity expenditure ($HE{E}_{s,m}$) was calculated by validating the multi-tiered piecewise limits defined in Section 2.1. The electricity rates compiled in Table 4 correspond to integrated final prices regulated by the Energy Regulatory Commission (CRE), which incorporate generation, transmission, distribution costs, and VAT, with baseline tiers receiving direct fiscal subsidies from the Federal Government via CFE Suministrador de Servicios Básicos.

Table 5. Electricity prices for residential tariffs by consumption block tier.

	Basic ($/kWh)	Intermediate ($/kWh)	Surplus ($/kWh)
January 2018	0.793	0.956	2.802
December 2024	1.059	1.285	3.763
Variation	33.54%	34.41%	34.30%

To demonstrate the mathematical sequencing, a validation case was executed for the baseline period of January 2018 in the state of Aguascalientes. With a synchronized annual demand profile yielding an monthly energy input of $E{C}_T=114 \mathrm{kWh}/\mathrm{month}$, the volumetric basic boundary ($L_B=75 \mathrm{kWh}$) and intermediate constraint ($L_I=65 \mathrm{kWh}$) defined for Tariff 1 coexisted as follows:

$$E{C}_B=\min \left(\mathrm{114,75}\right)=75 \mathrm{kWh}$$

$$E{C}_I=\max \left(0,\min \left(114-\mathrm{75,65}\right)\right)=39 \mathrm{kWh}$$

$$E{C}_S=\max (\mathrm{0,114}-\left(75+65\right))=0 \mathrm{kWh}$$

Mapping these block allocations to the historical rate metrics ($P_B=0.793$ MXN/kWh and $P_I=0.956$ MXN/kWh) yields the absolute financial transaction value via Equation (4):

$$CC{E}_m=\left(75·0.793\right)+\left(39·0.956\right)+\left(0·2.802\right)=96.76 \mathrm{MXN}$$

Replicating this computational optimization across all 32 federal entities enabled the cross-sectional evaluation of the static immediate energy budget burden ($EI{P}_{s,m}$), as shown in Table 6 for the initial multi-period checkpoint.

Table 6. Electricity costs by Mexican state under Tariff 1 for January 2018.

Mexican State	Total kWh	Basic			Intermediate			Surplus			Total
		kWh	$	$	kWh	$	$	kWh	$	$	$
Aguascalientes	114	75	0.793	59.475	39	0.956	37.284	0	2.802	0	96.759
Baja California	281	75	0.793	59.475	65	0.956	62.140	141	2.802	395.082	516.697
Chiapas	106	75	0.793	59.475	31	0.956	29.636	0	2.802	0	89.111
Chihuahua	161	75	0.793	59.475	65	0.956	62.140	21	2.802	58.842	180.457
City of México	98	75	0.793	59.475	23	0.956	21.988	0	2.802	0	81.463
Coahuila	198	75	0.793	59.475	65	0.956	62.140	58	2.802	162.516	284.131
Durango	107	75	0.793	59.475	32	0.956	30.592	0	2.802	0	90.067
México	79	75	0.793	59.475	4	0.956	3.824	0	2.802	0	63.299
Guanajuato	112	75	0.793	59.475	37	0.956	35.372	0	2.802	0	94.847
Guerrero	123	75	0.793	59.475	48	0.956	45.888	0	2.802	0	105.363
Hidalgo	77	75	0.793	59.475	2	0.956	1.912	0	2.802	0	61.387
Jalisco	116	75	0.793	59.475	41	0.956	39.196	0	2.802	0	98.671
Michoacán	116	75	0.793	59.475	41	0.956	39.196	0	2.802	0	98.671
Nuevo León	251	75	0.793	59.475	65	0.956	62.140	111	2.802	311.022	432.637
Oaxaca	98	75	0.793	59.475	23	0.956	21.988	0	2.802	0	81.463
Puebla	97	75	0.793	59.475	22	0.956	21.032	0	2.802	0	80.507
Querétaro	111	75	0.793	59.475	36	0.956	34.416	0	2.802	0	93.891
San Luis Potosí	129	75	0.793	59.475	54	0.956	51.624	0	2.802	0	111.099
Tlaxcala	93	75	0.793	59.475	18	0.956	17.208	0	2.802	0	76.683
Veracruz	139	75	0.793	59.475	64	0.956	61.184	0	2.802	0	120.659
Zacatecas	105	75	0.793	59.475	30	0.956	28.680	0	2.802	0	88.155
Cost Average											140.286
Tariff Average	130	75	0.793	59.475	55	0.956	52.58	0	2.802	0	112.055

By contrasting these calculated expenditure tracking points against the socioeconomic baseline ($MH{I}_{s,m}$), the multi-period evolutionary paths of the localized financial stress were obtained. Table 7 provides the explicit comparison of the calculated $EI{P}_{s,m}$ for the structural boundary constraints of Tariff 1 regimes between January 2018 and December 2024. The empirical evidence confirms an alarming, asymmetrical growth trend; the average national energy budget burden for Tariff 1 increased from $2.55\%$ to $3.57\%$, driven by localized cost increases that severely outpaced household compensation capacities.

Table 7. Comparison of the $EIP$ for Tariff 1 between January 2018 and December 2024.

Mexican State	2018				2024
	Total kWh	HEE	MHI	EIP	Total kWh	HEE	MHI	EIP
		$	$	%		$	$	%
Aguascalientes	114	97	6678	1.45	119	136	7485	1.82
Baja California	281	517	6660	7.76	345	934	8432	11.08
Chiapas	106	89	2983	2.98	106	119	3424	3.48
Chihuahua	161	180	6080	2.96	188	344	7697	4.47
City of México	98	81	8900	0.91	120	137	9224	1.49
Coahuila	198	284	6293	4.51	227	490	7304	6.71
Durango	107	90	4912	1.83	121	139	5799	2.40
México	79	63	5403	1.17	87	95	6180	1.54
Guanajuato	112	95	5192	1.83	124	142	6212	2.29
Guerrero	123	105	3301	3.18	155	219	4046	5.41
Hidalgo	77	61	4364	1.40	90	99	4955	2.00
Jalisco	116	99	6812	1.45	133	154	7267	2.12
Michoacán	116	99	4800	2.06	135	157	5686	2.76
Nuevo León	251	433	7760	5.58	258	607	9753	6.22
Oaxaca	98	81	3555	2.28	114	130	4335	3.00
Puebla	97	81	4386	1.85	113	128	5180	2.47
Querétaro	111	94	6903	1.36	128	148	8135	1.82
San Luis Potosí	129	111	5232	2.12	162	246	6100	4.03
Tlaxcala	93	77	4535	1.70	107	121	4903	2.47
Veracruz	139	121	3651	3.31	153	212	4419	4.80
Zacatecas	105	88	4272	2.06	117	133	5024	2.65
% Average				2.55				3.57
Cost Average		140	5365	2.61		233	6265	3.72
Tariff Average	130	112	5365	2.09	148	193	6265	3.08

* Note: All values (HEE and MHI) are expressed on a monthly basis (MXN/month). MHI represents the monthly household income derived by dividing the annual baseline (Table 3) by 12 months.

3.3. Econometric Consolidated Metrics and CAGR Calculation

To verify the non-linear behavior asserted at the beginning of this research, Table 8 presents the consolidated average metrics (Cost, Demand volume, and Percentage Impact) across the entire residential tariff spectrum (Tariffs 1 to 1F). Table 8 compiles the consolidated state-level averages categorized by residential tariff, where ‘Cost’ indicates monthly expenditure (MXN/month), ‘kWh’ reflects monthly consumption volume ($E{C}_T$, kWh/month), and ‘%’ represents the calculated Economic Impact Percentage ($EI{P}_{s,m}$).

Table 8. Compound Annual Growth Rate (CAGR_EB) and Annual Escalation Index (1+CAGR_EB) across Residential Tariff Categories in Mexico.

Average	Tariff
	1		1A		1B		1C		1D		1E		1F
	2018	2024	2018	2024	2018	2024	2018	2024	2018	2024	2018	2024	2018	2024
Cost	2.61	3.72	3.83	5.56	4.00	6.06	4.73	6.94	4.88	7.27	5.94	8.73	9.42	14.67
kWh	2.09	3.08	3.14	5.16	2.95	5.08	3.80	6.08	3.85	6.48	5.53	8.50	9.45	14.67
%	2.55	3.57	3.76	5.35	3.79	5.60	4.41	6.47	4.60	6.92	5.73	8.55	9.45	14.90

* Note: Values reflect state-level monthly averages for each tariff category: Cost (MXN/month), kWh (monthly energy input $E{C}_T$), and % (Socioeconomic Energy Burden $EI{P}_{s,m}$).

The quantitative discrepancies observed among the different structural averages validate that traditional linear index systems introduce significant tracking distortions, confirming that the localized socio-energetic percentage average ($\%$) constitutes the only unbiased input capable of anchoring the dynamic indicator architecture. By substituting these unweighted multi-period tracking points (${\overline{EIP}}_{t_0}$ and ${\overline{EIP}}_{t_f}$) into the exponential mathematical formulation defined in Equation (6), the Compound Annual Growth Rate of the household energy burden ($CAG{R}_{EB}$) was calculated for each tariff system over the $n=6$ years longitudinal duration. The normalized results are compiled in Table 9, proving that high-temperature regimes experience the most severe compound annual growth rates ($CAG{R}_{EB}=9.53\%$ for Tariff 1F).

Table 9. Annual Index for residential tariff structures in Mexico.

Annual Index				Tariff
	1	1A	1B	1C	1D	1E	1F
	1.06961	1.07308	1.08121	1.07968	1.08510	1.08333	1.09534

3.4. Dynamic Projections of the Energy-Economic Impact Index (EEII)

The determination of the $CAG{R}_{EB}$ enabled the deployment of the dynamic Energy-Economic Impact Index ($EEI{I}_t$) as an evolutionary forecasting instrument via Equation (8). To illustrate its prospective utility, Figure 3 demonstrates the dynamic tracking and projection paths of the $EEI{I}_t$ computed across a ten-year continuous horizon (2018–2027). Specifically, Figure 3a illustrates the continuous evaluation for temperate areas operating under the baseline bounds of Tariff 1, whereas Figure 3b outlines the compounding trajectory for regions subject to severe thermal gradients under the structural limits of Tariff 1D. The longitudinal trends illustrated in these subfigures visually demonstrate a critical, climate-induced socioeconomic vulnerability. As observed in Figure 3a, households categorized under Tariff 1 experience a managed escalation of their energy burden, with the projected index reaching an $EEI{I}_{2027}=4.673$. Conversely, Figure 3b reveals an aggressive compounding path where the domestic index climbs rapidly to $EEI{I}_{2027}=9.594$, absorbing a disproportionate share of the household budget. This explicit divergence between the two projection paths confirms that current IBT subsidy mechanisms are structurally insufficient to isolate vulnerable residential economics from rising seasonal energy demands, highlighting the predictive robustness of the developed $EEI{I}_t$ framework for strategic energy planning.

Figure 3. Application of the Annual Index to calculate the EEII for Tariff 1 (a) and Tariff 1D (b) from 2018 to 2027.

3.5. Methodological Guide for the Interpretation of the Socio-Energetic Space

This section provides a structured, step-by-step framework to correctly interpret the multi-dimensional mapping illustrated in Figure 4. The graphical space avoids traditional flat tabular constraints by simultaneously coupling macroeconomic capacities, regulatory tariff design, and compound temporal trajectories.

Figure 4. Socio-Energetic Space Map: Structural Burden Trajectories and Regional Polarization.

Step 1: Establishing the Coordinate Boundaries (Axes Alignment)

(1)

The Horizontal Axis (Economic Baseline): The reader should first observe the horizontal displacement, which evaluates the Average Monthly Household Income ($\mathit{M}\mathit{H}{\mathit{I}}_{\mathit{s},\mathit{m}}$ expressed in MXN). Movement toward the right quadrant indicates states with higher purchasing power and robust macroeconomic dynamics (e.g., Nuevo León), whereas clustering toward the left quadrant isolates lower-income regional contexts (e.g., Chiapas).

(2)

The Vertical Axis (Financial Stress): The vertical displacement tracks the Socioeconomic Energy Burden ($\mathit{E}\mathit{I}{\mathit{P}}_{\mathit{s},\mathit{m}}$ expressed in %). A higher position on this axis indicates that residential electricity costs absorb a critical, disproportionate percentage of the household budget, directly signaling vulnerability and energy poverty risks.

Step 2: Decoding Symmetries via Marker Shapes and Colors

(3)

Temporal Horizon Markers: The temporal dimension is operationalized through geometric variations. The circular markers (●) act as the definitive baseline anchor representing the country’s socio-energetic state in 2018. Conversely, the triangular markers (▲) denote the terminal status of the entities during 2024.

(4)

Climatic and Tariff Severity: The color spectrum serves as a proxy for regional thermal gradients. The visualization transits from cold blue and green tones—which represent temperate geographic zones under Tariff 1—toward aggressive yellow, orange, and red indicators that isolate extreme high-temperature jurisdictions governed by Tariffs 1E and 1F.

Step 3: Vector Trajectory and Polarization Analysis

(5)

Horizontal Vectors (Balanced Development): States experiencing balanced growth display horizontal vector arrows that move smoothly to the right with minimal vertical slope. This indicates that local household income increases successfully neutralized electricity price adjustments. As observed, Tariff 1 regions remain stable within the lower boundaries of the map ($\mathit{E}\mathit{I}{\mathit{P}}_{\mathit{s},\mathit{m}}\mathbf{<}\mathbf{4}\mathbf{\%}$).

(6)

Vertical Vectors (The Regressive Trap): The most critical phenomena are identified by steep, near-vertical upward vector arrows, prominently displayed by orange and red markers (Tariffs 1E and 1F). These lines prove that even when a state achieved macroeconomic progress (horizontal shifting to the right), the climate-induced demand spikes and the aggressive steps of the Increasing Block Tariff (IBT) system forced households into high-stress economic coordinates.

Ultimately, this step-by-step reading confirms that the developed Energy-Economic Impact Index framework successfully captures regional polarization, proving that current electricity pricing structures exert a highly regressive financial pressure on the warmest and most vulnerable sectors of the population.

3.6. Cross-Tariff Density Analysis via Subscribed Thermal Gradient Maps

To comprehensively synthesize the extensive state-by-state micro-data configurations, a standardized socio-energetic density matrix was deployed. Figure 5 illustrates the consolidated cross-sectional intensity of the Socioeconomic Energy Burden ($\mathit{E}\mathit{I}{\mathit{P}}_{\mathit{s},\mathit{m}}$) stratified simultaneously by state jurisdiction and regulatory climate brackets.

Figure 5. Spatiotemporal evolution of the Socioeconomic Energy Burden (EIPs,m) across Mexican states (2018–2024).

The heat distribution confirms that geographic positioning acts as an unequal forcing driver. While temperate territories maintain stable, low-intensity configurations across basic brackets ($\mathit{E}\mathit{I}{\mathit{P}}_{\mathit{s},\mathit{m}}\mathbf{<}\mathbf{4}\mathbf{\%}$), the socio-energetic intersection within extreme thermal zones triggers an acute regional critical cluster. This graphical matrix validates that isolating state aggregates into visual density clusters provides higher diagnostic performance for policy target identification than flat multi-page tabular records.

3.7. Macroeconomic Asymmetry and Divergence Indicators

The structural polarization identified throughout the multi-period tracking horizon is ultimately driven by a severe growth trajectory mismatch. Figure 6 introduces a dynamic comparative framework confronting the Compound Annual Growth Rate of localized household incomes (${\mathrm{CAGR}}_{\mathit{M}\mathit{H}\mathit{I}}$) against the parallel compound escalation rate of the financial energy burden (${\mathrm{CAGR}}_{\mathit{E}\mathit{B}}$) across the entire residential tariff spectrum.

Figure 6. Macroeconomic Growth Mismatch: Income Trajectories vs. Energy Burden Escalation.

The empirical divergence illustrated in Figure 6 clarifies the asymmetric nature of the system. Within Tariff 1 environments, income trajectories closely matched pricing updates, preserving household affordability boundaries. However, as the regulatory framework shifts toward warmer regimes (Tariffs 1E and 1F), the energy budget burden parameters exhibit an exponential expansion that dramatically outpaces real domestic income growth. This diagnostic layout confirms that increasing block tariff architectures systematically dilute macroeconomic regional progress under severe climate stress.

4. Discussion

4.1. Tariff Asymmetry and the Regressive Subsidy Paradox

The cross-sectional tracking and dynamic spatial indicators compiled throughout this study expose a fundamental paradox within the Mexican residential electricity subsidy framework. Theoretically, the volumetric adjustments embedded within the Increasing Block Tariff (IBT) system are designed to protect domestic economies by expanding subsidized thresholds as a function of regional summer temperatures. However, the empirical synchronization of econometric data proves that this mechanism introduces a highly regressive financial pressure. When confronting these findings against classical economic theory, such as the non-linear redistributive models established by Borenstein [27], it becomes evident that pricing mechanisms based strictly on physical climate boundaries fail to isolate socioeconomic vulnerabilities. As demonstrated by the vertical trajectories in the socio-energetic map (Figure 4) and the thermal density matrix (Figure 5), low-income states subject to high thermal regimes (e.g., Sinaloa and Sonora) undergo an accelerated financial stress that absorbs up to 14.90% of their monthly income, effectively trapping vulnerable populations in high-vulnerability coordinates. Empirical evidence of this regressivity is reflected in how public fiscal resources are distributed under high-temperature regimes (e.g., Tariffs 1E and 1F). Because volumetric subsidy limits (${\mathit{L}}_{\mathit{B}}$ and ${\mathit{L}}_{\mathit{I}}$) expand automatically with regional temperature thresholds regardless of household socioeconomic status, wealthier income deciles absorb a disproportionate share of the absolute subsidy budget. Higher-income households typically reside in larger dwellings and possess greater purchasing power to deploy multiple high-capacity air conditioning units, thereby consuming large volumes of energy within expanded subsidized tiers. Conversely, lower-income households in the same warm regions absorb high financial stress ($\mathit{E}\mathit{I}{\mathit{P}}_{\mathit{s},\mathit{m}}$ reaching up to 14.90%) because their limited income capacity cannot offset energy costs even within subsidized blocks, highlighting a structural misallocation of public funds.

4.2. Climate-Driven Energy Vulnerability in Transition Economies

The non-linear growth trajectories modeled via the Compound Annual Growth Rate ($\mathit{C}\mathit{A}\mathit{G}{\mathit{R}}_{\mathit{E}\mathit{B}}$) demonstrate that tariff escalation systematically outpaces baseline household income growth across residential tiers, compounding the structural energy burden over time. The structural mismatch illustrated in Figure 6 clarifies that regional income variations are completely insufficient to absorb the dynamic escalation of electricity prices under severe climate stress. This phenomenon expands the conventional definition of energy poverty described in transition economies by authors like Faiella and Lavecchia [22] and Heindl [30]. Beyond statistical correlation, the relationship between regional thermal regimes, tariff structures, and household energy burdens exhibits a clear structural causality. Exogenous increases in regional maximum temperatures directly drive the physical requirement for indoor space cooling, accelerating monthly electricity consumption ($\mathit{E}{\mathit{C}}_{\mathit{T}}$). As households deploy cooling systems, their consumption volume exceeds the basic (${\mathit{L}}_{\mathit{B}}$) and intermediate (${\mathit{L}}_{\mathit{I}}$) volumetric thresholds, causally forcing energy demand into surplus tiers ($\mathit{E}{\mathit{C}}_{\mathit{S}}$) where government subsidies are phased out and marginal unit prices (${\mathit{P}}_{\mathit{S}}$) scale non-linearly. Consequently, climate operates not merely as a descriptive baseline parameter, but as an active physical driver that interacts with the piecewise pricing architecture to generate asymmetric financial stress. In the Mexican context, energy vulnerability is not merely a restriction of access to the electrical grid, but a dynamic financial risk forced by the interaction of rising seasonal temperatures and multi-tiered price adjustments. The progressive degradation of affordability boundaries in the warmest states confirms that climate change operates as an active macroeconomic forcing parameter that amplifies regional polarization.

4.3. Policy Implications for Adaptive Subsidy Modernization

The empirical constraints mapped by the $\mathit{E}\mathit{E}\mathit{I}{\mathit{I}}_{\mathit{t}}$ framework provide vital decision-support benchmarks for energy policy planners aiming to optimize fiscal allocation and mitigate energy poverty risks. Currently, the Mexican residential subsidy model operates under a static, climate-centric allocation paradigm that overlooks localized socioeconomic gradients. The evidence compiled in this research demonstrates that this decoupling produces severe targeting inefficiencies. High-income segments with greater appliance acquisition capacities absorb a disproportionate share of public resources, while vulnerable household economics in extreme thermal zones face regressive financial traps.

To resolve these targeting deficiencies and transition toward an adaptive subsidy modernization strategy, regulatory authorities should pivot from allocation mechanisms based strictly on regional temperature thresholds to dynamic socio-energetic frameworks. Specifically, policy planners should implement three core structural updates:

Hybrid Socio-Energetic Subsidies: Replace pure temperature-dependent rules with a hybrid targeting index that cross-references regional thermal gradients with state-level household income deciles, ensuring that expanded baseline volumetric thresholds (${\mathit{L}}_{\mathit{B}}$ and ${\mathit{L}}_{\mathit{I}}$) are restricted exclusively to lower-income deciles in hyper-warm regions.

EEII Affordability Caps: Establish normative affordability boundaries by capping the maximum allowable household energy burden at a critical threshold (e.g., $\mathit{E}\mathit{E}\mathit{I}\mathit{I}\le \mathbf{5}\mathbf{\%}$ or $\mathbf{6}\mathbf{\%}$), triggering targeted fiscal relief when extreme seasonal heat forces vulnerable domestic economies past sustainable financial boundaries.

Capital Investment Reallocation: Transition away from deploying permanent, passive price-transfer subsidies for higher-income segments—which structurally constraint the state’s fiscal capacity—and dynamically reallocate public funds toward direct capital investments. Geographical coordinates identified under severe economic distress should be prioritized for targeted public programs, such as the direct deployment of residential photovoltaic (PV) microgeneration and energy-efficient thermal insulation retrofits for low-income dwellings.

Ultimately, shifting from a passive temperature-dependent pricing model to a dynamic socio-energetic indicator architecture establishes a progressive, efficient, and equitable framework capable of insulating vulnerable populations from climate-induced financial risks.

5. Conclusions

This study developed a robust mathematical and heuristic framework to systematically construct the Energy-Economic Impact Index (EEIIt), providing a multi-dimensional metric that quantifies the real financial pressure exerted by residential electricity billing on household socio-economic configurations. By integrating increasing block tariff structures, localized energy expenditures, and biennial household income datasets, the proposed indicator successfully bypasses the diagnostic limitations of traditional flat linear indices. The methodology established herein constitutes a highly replicable decision-support tool capable of managing the structural complexities of transition power sectors characterized by high climatic gradients and multi-tiered price adjustments. The longitudinal empirical validation spanning the 2018–2024 period yields three critical regulatory findings:

Asymmetric Financial Polarization: Residential electricity cost escalations systematically outpaced localized household income growth trajectories across the entire national territory. This structural mismatch caused a severe, non-linear polarization of the financial energy burden parameters.

The Climate Stress Regressive Trap: While temperate zones under Tariff 1 maintained stable affordability boundaries (EIPs,m < 4%), populations restricted by hyper-warm regimes (Tariffs 1D, 1E, and 1F) experienced severe vertical vulnerabilities. The intersection of climate-induced demand spikes and rigid block thresholds forced lower-income states into high-stress economic coordinates, capturing a peak energy budget burden of up to 14.90% in 2024.

Subsidies Targeting Inefficiencies: The current allocation framework based strictly on temperature boundaries generates regressive targeting issues. The preferential schemes are insufficient to isolate vulnerable household economics from rising seasonal demands, frequently subsidizing higher-income segments with larger cooling capabilities while penalizing lower-income deciles who lack thermal insulation or energy-efficient technologies.

Finally, the incorporation of the Annual Index introduces a flexible, predictive instrument to project EEIIt pathways under diverse prospective pricing scenarios or demographic shifts. The dynamic indicators developed in this work demonstrate that a transition toward an adaptive subsidy modernization paradigm—shifting from passive temperature-dependent rules to hybrid socio-energetic indicator metrics—is vital to optimize fiscal resource allocation, successfully reduce localized energy poverty gaps, and protect vulnerable households from climate-induced macroeconomic risks.

Furthermore, while this study demonstrates the necessity of capturing extreme consumption phenomena, formulating a closed-form dynamic optimization model to explicitly integrate the Extreme Energy Intensity Indicator ($EEI{I}_t$) into regulatory tariff threshold adjustments remains an essential avenue for future research.