# Can Energy Efficiency Promote Human Development in a Developing Economy?

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Literature Review: Energy Vis-à-Vis Human Development and Per Capita GDP

#### 2.1. Energy Efficiency and Access to Energy: Energy Poverty and Human Development

#### 2.1.1. Economic Growth–Energy Nexus

#### 2.1.2. The Role of Electricity in the Indian Context

## 3. Data and Methodology: The Nexus between Electricity Sector and Per Capita GDP in India

_{2t}= f (EFF

_{t}, GEL

_{t}, GEK

_{t}, LX

_{1t})

_{2}) for India is postulated to be a function efficiency in electricity generation (EFF), GEL is growth in labor input, GEK is growth of capital input for generating electricity in India. LX

_{1}is the KOF index of globalization in India by a year see [64] (The extant models specify in Equation (1) that human development—proxied by either human development index or human capital or health and education outcomes—is a function of economic growth, energy variables and other control variables (see [13], Equation (3), on p. 3). The control variables are meant to represent important structural changes within an economy due to financial development, urbanization, foreign direct investment, trade, and remittances. For the purpose of a parsimonious model, we choose the KOF globalization index as an explanatory variable that captures the inner dynamics of a country and its evolving relationships with the rest of the world). This index of globalization is a summary measure of economic, political and social dimensions of transformation due to globalization in a country see [64]. Annual data on the Indian economy over the period 1980 to 2017 are collected from the Reserve Bank of India (RBI)’s Handbook of Statistics, RBI-KLEMS database. Equation (1) is linearized and written as—after converting X

_{2}, GEK, and GEL into their (natural) logarithmic values:

_{2t}= γ

_{0}+ γ

_{1}EFF

_{t}+ γ

_{2}GEL

_{t}+ γ

_{3}GEK

_{t}+ γ

_{4}LX1

_{t}+ u

_{t}

_{t}, and the parameters that need to be estimated are γs.

#### 3.1. Estimation Strategy

#### 3.1.1. Basic Statistical Properties of the Data Series:

#### 3.1.2. ARDL Modeling for Estimation

_{0}. In this regard, the short-run coefficient for the mentioned variables in our study are presented by α

_{i}(i = 1, 2, …, 5), and the parameter βi (for i = 1, 2, …, 5) is the long-run coefficient; lastly, the disturbance term is presented by ε

_{t}.

_{1}= α

_{2}= … = α

_{5}= 0. Therefore, the above-mentioned null hypothesis suggests the presence of no cointegration; on the other hand, the alternative one asserts for a cointegration. In other words, alternative hypothesis (H1) postulates that a minimum one parameter of αi is not zero. The Wald test will be used to compare the F-statistics to the critical values of [65]. Now, if the computed F-statistics are greater than the critical value’s upper bound, the ARDL mechanism detects cointegration.

#### 3.1.3. Novel Dynamic ARDL Simulations: An Extension

_{2t}= α

_{0}+ α

_{1}∆EFF

_{t}+ α

_{2}∆GEK

_{t}+ α

_{3}∆GEL

_{t}+ α

_{4}∆LX

_{1t}+ β

_{1}EFF

_{t-1}+ β

_{2}GEK

_{t-1}+ β

_{3}GEL

_{t-1}+ β

_{4}LX

_{1t-1}+ ε

_{t}

_{0}. Other α

_{i}s are the coefficients for the short run and β

_{i}s are the coefficients for the long run of the ARDL model, as stated in the context of Equation (3).

#### 3.1.4. Frequency Domain Causality Test

_{2}) through the frequency domain causality test approach. The frequency domain causality test method of [22], as [39] emphasize, will enable us to confirm the stability condition of the model. Regarding the relevant variables of our model, namely—efficiency in electricity generation (EFF) and economic development (X

_{2})—the equation of the frequency domain causality test is reduced to:

_{2t}= θ

_{0}+ θ

_{1}X

_{2t-1}+ θ

_{2}X

_{2t-2}+ …. + θ

_{p}X

_{2t-p}+ λ

_{1}EFF

_{t-1}+ λ

_{2}EFF

_{t-2}+ …. + λ

_{p}EFF

_{t-p}+ error

_{t}

_{i}s and λ

_{i}s to assess the postulated causal flows running from EFF to X

_{2}and error

_{t}is the error term.

## 4. Results and Discussion

_{2}). The AIC was utilized in this work to identify an appropriate lag for the ARDL model based on the restricted observations. The calculated ARDL model, as shown in Table 3, has passed various diagnostic tests (see Table 4).

_{1}) of the aggregate Indian economy, which is significant at a 1% level of significance. Now, the ARDL estimates clears all diagnostic tests, according to Table 4.

_{2}(human development). For the ECM, we undertook the Wald Test to examine the joint significance of the coefficients of this estimated model, which notes that all the variables are jointly significant in short run (see Appendix A.2). In long-run analysis, as presented in Table 6, we find that the independent variables—except GEK—are significant at least at the 5% level. From Table 6, we have the critical finding: ceteris paribus, the efficiency in the generation of electricity sector drives the per capita GDP in India. We note that the per capita GDP (X

_{2}) bears an inverse and statistically significant relationship with GEK and GEL.

#### 4.1. Evidence from Novel Dynamic ARDL Simulations: Discussion

_{2}), in the long run. The short-run effect is not statistically significant. The effects of EFF from the standard ARDL model, given in Table 5 and Table 6, are different from the findings from the novel dynamic ARDL simulations mainly for the short run. The result is in consonance with the findings of [71]. This result has a special significance for the finding of [13], who note that energy access worsens human development in South Asia. This is feasible, as our work highlights, if energy efficiency declines with rising energy access. We, hence, provide an economic rationale for the findings of [13].

_{1}) has a strong and positive short-run effect on human development, although there is no evidence of any long-run impact. The effect of globalization from the standard ARDL model is different from the predictions of the dynamic ARDL simulations for the long run. The dynamic ARDL model concurs with the findings of [13] that the external sector promotes human development.

_{2}) is explained by the chosen regressors. The projected F-statistics and the associated P value show that the proposed model is a good fit.

_{2}). Figure 4 and Figure 5, respectively, trace the impulse response plot of human development following 10% decreases and 10% increases in growth in labor input and capital input in the electricity sector. Figure 6 shows the impulse response plot of human development following a 10% decrease/increase in the globalization index (LX

_{1}) for India.

_{2}). Once again, the dots specify average prediction value, whereas the dark blue to light blue line specifies 75, 90, and 95% confidence interval, respectively. Interestingly, ceteris paribus, the first panel of Figure 4 traces the adverse impacts of increases in the use of labor input in the electricity sector on human development. In the short run, there is an adverse and statistically significant, impact on human development, which can be confirmed from the first panel of Figure 4. The long-run effect is not statistically significant. The second panel of Figure 4 confirms the positive impact of a 10% decrease in GEL on human development. This effect is statistically significant in the short run but not in the long run.

_{1}), ceteris paribus in year t = 10, on subsequent human development. The first panel shows that an increase in the globalization index, ceteris paribus, by 10% raises human development in India. The short-run effect is positive and statistically significant, while the effect in the long run is also positive but marginally significant. The effect of globalization is re-confirmed from the second panel of Figure 6 when we consider a 10% decrease in globalization in year t = 10.

#### 4.2. Causality from Frequency Domain Analysis

_{2}) using the frequency domain causality test. For obtaining the frequency domain causality test results, we apply the Breitung–Candelon Spectral Causality approach [22]. Figure 7 describes the findings from the Breitung–Candelon Spectral Causality. The first panel of Figure 7 confirms that energy efficiency causes human development. The second panel of Figure 7 confirms that there is no reverse causality running from human development to energy efficiency.

_{2}). Consequently, if efforts to expand energy access leads to a decrease in EFF and increases in GEK and GEL—such changes in the electricity sector can seriously compromise the per capita GDP (X

_{2}). This effect on X

_{2}can thereby compromise energy affordability—or purchasing power to buy an adequate energy bundle. This will, in turn, lower human development. This chain of events can be called the vicious cycle of energy access. On the contrary, if improving energy access is achieved by increases in EFF and decreases in GEK and GEL, we are in a virtuous cycle: as improved energy access improves EFF as well as lowers GEK and GEL—the per capita GDP (X

_{2}) rises. Such increases in X

_{2}leads to improvements in human development—ceteris paribus. The impact of globalization seems to have a positive and highly significant effect on per capita GDP.

## 5. Conclusions

- There is a long-term relationship between per capita GDP vis-à-vis energy efficiency in the electricity sector, growth in labor and capital inputs in the electricity sectors.
- Increases (decreases) in energy efficiency in the generation of electricity increases (decreases) per capita GDP while increases (decreases) in the growth in labor and capital inputs, ceteris paribus, decrease (increase) per capita GDP.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Data Availability Statement

## Conflicts of Interest

## Appendix A

#### Appendix A.1. ARDL Framework (*: Prob. labels Probability.)

Dependent Variable: X_{2} | ||||

Regressors: EFF GEL GEK LX_{1} | ||||

Selected Model: ARDL(4, 4, 3, 4, 0) | ||||

Variable | Coefficient | Std. Error | t-Statistic | Prob. * |

X_{2t-1} | 0.603497 | 0.187851 | 3.212642 | 0.0063 |

X_{2t-2} | –0.153736 | 0.225854 | –0.680686 | 0.5072 |

X_{2t-3} | 0.160872 | 0.216136 | 0.744311 | 0.4690 |

X_{2}t-4 | 0.230195 | 0.172829 | 1.331924 | 0.2042 |

EFF_{t} | –0.017044 | 0.506318 | –0.033662 | 0.9736 |

EFF_{t-1} | –0.482704 | 0.685688 | –0.703969 | 0.4930 |

EFF_{t-2} | 0.782958 | 0.754942 | 1.037110 | 0.3173 |

EFF_{t-3} | 0.192513 | 0.691596 | 0.278361 | 0.7848 |

EFF_{t-4} | 0.529368 | 0.441430 | 1.199211 | 0.2503 |

GEL_{t} | –0.145987 | 0.037187 | –3.925763 | 0.0015 |

GEL_{t-1} | 0.028081 | 0.050136 | 0.560085 | 0.5843 |

GEL_{t-2} | –0.028900 | 0.047127 | –0.613237 | 0.5496 |

GEL_{t-3} | 0.078590 | 0.036267 | 2.166951 | 0.0480 |

GEK_{t} | –0.017350 | 0.040497 | –0.428440 | 0.6748 |

GEK_{t-1} | –0.002427 | 0.043862 | –0.055334 | 0.9567 |

GEK_{t-2} | –0.052846 | 0.044827 | –1.178867 | 0.2581 |

GEK_{t-3} | –0.071320 | 0.045506 | –1.567283 | 0.1394 |

GEK_{t-4} | 0.053284 | 0.029512 | 1.805535 | 0.0925 |

LX_{1t} | 0.471149 | 0.174281 | 2.703395 | 0.0171 |

C | –1.365483 | 0.361020 | –3.782289 | 0.0020 |

R-squared | 0.999648 | Mean dependent var | 6.784791 | |

Adjusted R-squared | 0.999169 | S.D. dependent var | 0.434605 | |

S.E. of regression | 0.012527 | Akaike info criterion | –5.632638 | |

Sum squared resid | 0.002197 | Schwarz criterion | –4.734779 | |

Log likelihood | 115.7548 | Hannan–Quinn criter. | –5.326442 | |

F-statistic | 2089.674 | Durbin–Watson stat | 2.008306 | |

Prob(F-statistic) | 0.000000 |

#### Appendix A.2. Wald Test for Joint Significance of Variables

Test Statistic | Value | df | Probability |

F-statistic | 2089.674 | (19,14) | 0.0000 |

Chi-square | 39,703.81 | 19 | 0.0000 |

**X**

_{2}Test Statistic | Value | df | Probability |

F-statistic | 26.80753 | (4,14) | 0.0000 |

Chi-square | 107.2301 | 4 | 0.0000 |

**EFF**

Test Statistic | Value | df | Probability |

F-statistic | 2.792200 | (5,14) | 0.0594 |

Chi-square | 13.96100 | 5 | 0.0159 |

**GEL**

Test Statistic | Value | df | Probability |

F-statistic | 6.156406 | (4,14) | 0.0045 |

Chi-square | 24.62562 | 4 | 0.0001 |

**GEK**

Test Statistic | Value | df | Probability |

F-statistic | 2.477837 | (5,14) | 0.0830 |

Chi-square | 12.38919 | 5 | 0.0298 |

Test Statistic | Value | df | Probability |

t-statistic | 2.703395 | 14 | 0.0171 |

F-statistic | 7.308346 | (1,14) | 0.0171 |

Chi-square | 7.308346 | 1 | 0.0069 |

**LX**

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**Figure 3.**The Impulse Response Plots: (

**a**) Impact of increase in Efficiency (EFF) on Human Development (X

_{2}) and (

**b**) Impact of decrease in Efficiency (EFF) on Human Development (X

_{2}).

**Figure 4.**The Impulse Response Plots: (

**a**) Impact of increase in the growth of Labor input (GEL) on Human Development (X

_{2}); (

**b**) Impact of decrease in the growth of Labor input (GEL) on Human Development (X

_{2}).

**Figure 5.**The Impulse Response Plots: (

**a**) Impact of increase in the growth of Capital Input (GEK) on Human Development (X

_{2}); (

**b**) Impact of decrease in the growth of Capital Input (GEK) on Human Development (X

_{2}).

**Figure 6.**The Impulse Response Plots: (

**a**) Impact of the increase in Globalization (LX1) on Human Development Index (X

_{2}); (

**b**). Impact of the decrease in Globalization (LX1) on Human Development Index (X

_{2}).

**Figure 7.**Spectral Causality Plots:(

**a**) H

_{0}: Efficiency (EFF) does not causing the Human Development (X

_{2}); (

**b**) H

_{0}: Human Development (X

_{2}) does not causing the Efficiency (EFF).

Tests | X_{2} | EFF | GEL | GEK | LX_{1} |
---|---|---|---|---|---|

Mean | 6.711728 | 0.814003 | 0.356346 | 0.423923 | 3.712708 |

Median | 6.663949 | 0.814159 | 0.417855 | 0.434673 | 3.621348 |

Maximum | 7.594553 | 0.895847 | 0.704378 | 0.699802 | 4.131051 |

Minimum | 6.047145 | 0.771561 | 0 | 0 | 3.430381 |

Std. Dev | 0.463878 | 0.030415 | 0.256177 | 0.165195 | 0.265169 |

Skewness | 0.311098 | 0.561938 | –0.28758 | –0.20378 | 0.398794 |

Kurtosis | 1.87854 | 2.834384 | 1.525773 | 2.492414 | 1.515318 |

ADF Test Results | ||||||||
---|---|---|---|---|---|---|---|---|

Variables | Intercept | Trend and Intercept | ||||||

I (0) | I (1) | I (0) | I (1) | |||||

t-Value | Prob. | t-Value | Prob. | t-Value | Prob. | t-Value | Prob. | |

X_{2} | 3.549883 | 1.0000 | –4.637527 | 0.0007 | 0.620532 | 0.9717 | –5.271436 | 0.0012 |

EFF | –1.780784 | 0.3835 | –4.066172 | 0.0032 | –1.122112 | 0.9114 | –4.592234 | 0.0041 |

GEL | –1.912871 | 0.3230 | –7.406791 | 0.0000 | –1.880689 | 0.6443 | –7.524634 | 0.0000 |

GEK | –3.595382 | 0.0107 | –10.83325 | 0.0000 | –3.876973 | 0.0233 | –10.42670 | 0.0000 |

LX_{1} | 1.120773 | 0.9970 | –3.703450 | 0.0082 | –2.530046 | 0.3128 | –3.996790 | 0.0178 |

PP Test Results | ||||||||

X_{2} | 15.93985 | 1.0000 | –4.637527 | 0.0007 | 0.727703 | 0.9995 | –15.13879 | 0.0000 |

EFF | –2.874343 | 0.0581 | –4.051137 | 0.0033 | –1.420426 | 0.8381 | –4.590645 | 0.0041 |

GEL | –2.285087 | 0.1819 | –7.195004 | 0.0000 | –2.266149 | 0.4410 | –7.575756 | 0.0000 |

GEK | –3.909380 | 0.0047 | –10.83352 | 0.0000 | –4.212910 | 0.0104 | –10.42670 | 0.0000 |

LX_{1} | 0.625066 | 0.9886 | –3.750291 | 0.0073 | –1.833926 | 0.6678 | –4.068937 | 0.0150 |

ARDL Model | F-Statistics | CV 1% | CV 5% | |||
---|---|---|---|---|---|---|

I(0) | I(1) | I(0) | I(1) | |||

X_{2}, EFF, GEL, GEK, LX_{1} | (4,4,3,4,0) | 9.1915024 | 3.29 | 4.37 | 2.56 | 3.49 |

Diagnostic Test | Chi^{2} (p-Value) | Result |
---|---|---|

Breusch–Godfrey LM | 0.8934 | Serial correlation problem is not found |

Breusch–Pagan–Godfrey | 0.8728 | Heteroscedasticity problem is not found |

Ramsey RESET Test | 0.7605 | Correct model specification |

Jarque–Bera Normality Test | 0.9326 | Normal distribution of the residual |

ECM Regression | ||||
---|---|---|---|---|

Restricted Constant and No Trend | ||||

Variable | Coefficient | Std. Error | t-Statistic | Prob. |

∆X_{2t-1} | –0.237332 | 0.143138 | –1.658061 | 0.1195 |

∆X_{2t-2} | –0.391068 | 0.129973 | –3.008834 | 0.0094 |

∆X_{2t-3} | –0.230195 | 0.105675 | –2.178330 | 0.0470 |

∆EFF_{t} | –0.017044 | 0.351853 | –0.048440 | 0.9621 |

∆EFF_{t-1} | –1.504839 | 0.380388 | –3.956058 | 0.0014 |

∆EFF_{t-2} | –0.721881 | 0.341624 | –2.113087 | 0.0530 |

∆EFF_{t-3} | –0.529368 | 0.339985 | –1.557035 | 0.1418 |

∆GEL_{t} | –0.145987 | 0.023160 | –6.303292 | 0.0000 |

∆GEL_{t-1} | –0.049690 | 0.027333 | –1.817951 | 0.0905 |

∆GEL_{t-2} | –0.078590 | 0.026600 | –2.954483 | 0.0105 |

∆GEK_{t} | –0.017350 | 0.028731 | –0.603891 | 0.5556 |

∆GEK_{t-1} | 0.070881 | 0.026647 | 2.660030 | 0.0187 |

∆GEK_{t-2} | 0.018036 | 0.027107 | 0.665367 | 0.5166 |

∆GEK_{t-3} | –0.053284 | 0.018434 | –2.890580 | 0.0119 |

ECT/CointEq_{t-1} * | –0.159171 | 0.018398 | –8.651306 | 0.0000 |

R-squared | 0.837222 | Mean dependent var | 0.042773 | |

Adjusted R-squared | 0.717280 | S.D. dependent var | 0.020224 | |

S.E. of regression | 0.010753 | Akaike info criterion | –5.926756 | |

Sum squared resid | 0.002197 | Schwarz criterion | –5.253361 | |

Log likelihood | 115.7548 | Hannan–Quinn criter. | –5.697109 | |

Durbin–Watson stat | 2.008306 |

Levels Equation Dependent Variable (X _{2}) | ||||
---|---|---|---|---|

Restricted Constant and No Trend | ||||

Variable | Coefficient | Std. Error | t-Statistic | Prob. |

EFF | 6.314552 | 2.434584 | 2.593689 | 0.0212 |

GEL | –0.428576 | 0.161478 | –2.654089 | 0.0189 |

GEK | –0.569571 | 0.329425 | –1.728984 | 0.1058 |

LX_{1} | 2.960027 | 0.708177 | 4.179784 | 0.0009 |

C | –8.578739 | 4.005812 | –2.141573 | 0.0503 |

EC = X_{2} − (6.3146 × EFF − 0.4286 × GEL − 0.5696 × GEK + 2.9600 × LX_{1} − 8.5787) |

∆X_{2} | Coef. | Std. Err. | T | p > t |
---|---|---|---|---|

ECT^ | –0.18 | 0.08 | –2.25 ** | 0.04 |

L1_X_{2} | –0.04542 | 0.050588 | –0.9 | 0.377 |

∆_EFF | –0.0547 | 0.437537 | –0.13 | 0.901 |

L1_EFF | 0.391297 | 0.166946 | 2.34 ** | 0.027 |

∆_GEL | –0.12877 | 0.035345 | –3.64 *** | 0.001 |

∆_GEK | 0.018681 | 0.032272 | 0.58 | 0.567 |

∆_LX_{1} | 0.435469 | 0.157616 | 2.76 ** | 0.01 |

L1_GEL | –0.01679 | 0.023562 | –0.71 | 0.482 |

L1_GEK | –0.04934 | 0.029101 | –1.7 | 0.102 |

L1_LX_{1} | 0.147595 | 0.096101 | 1.54 | 0.136 |

_cons | –0.49854 | 0.178583 | –2.79 ** | 0.01 |

R-Squared | 0.5593 | F | 3.81 | |

Adj. R-Squared | 0.4124 | Prob>F | 0.0033 | |

Number of Obs. | 37 | Simulations | 1000 |

Direction of Causality | Very Long Term | Long Term | Medium Term | Short Term | Very Short Term |
---|---|---|---|---|---|

ω = 0.05 | ω = 0.85 | ω = 1.65 | ω = 2.45 | ω = 3.14 | |

EFF => X_{2} | 2.8490 (0.2406) | 6.1981 (0.0451) ** | 2.3144 (0.3144) | 0.9292 (0.6284) | 1.5618 (0.4580) |

X_{2} => EFF | 0.3577 (0.8362) | 0.6555 (0.7206) | 3.6247 (0.1633) | 2.1802 (0.3362) | 1.9703 (0.3734) |

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**MDPI and ACS Style**

Gangopadhyay, P.; Das, N.
Can Energy Efficiency Promote Human Development in a Developing Economy? *Sustainability* **2022**, *14*, 14634.
https://doi.org/10.3390/su142114634

**AMA Style**

Gangopadhyay P, Das N.
Can Energy Efficiency Promote Human Development in a Developing Economy? *Sustainability*. 2022; 14(21):14634.
https://doi.org/10.3390/su142114634

**Chicago/Turabian Style**

Gangopadhyay, Partha, and Narasingha Das.
2022. "Can Energy Efficiency Promote Human Development in a Developing Economy?" *Sustainability* 14, no. 21: 14634.
https://doi.org/10.3390/su142114634