Renewable electricity supply, infrastructure, and gains from international trade in electric current

ABSTRACT

113 countries report producing electricity from renewable sources and participate in the global energy transition. On the one hand more cables have been built recently; but on the other hand some countries are blocking electricity shocks technologically as supply shocks undermine the insurance function of their markets. This paper shows through a dynamic panel data analysis that the higher share of renewables in electricity production has increased imports and decreased exports of electric currents. This shows that trade currently helps dealing with fluctuations of supply, but temporary losses for recipients of shocks may require payments to keep the borders open. Keywords: Gains from trade, electric current, gravity, infrastructure.

KEYWORDS:

• Gains from trade
• electric current
• infrastructure
• gravity
• GMMSYS

JEL CLASSIFICATION:

• F15
• F18
• F42
• H54

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

¹ Results indicated here hold for price elasticity near unity. Newbery and Stiglitz (1984) discuss modifications in greater detail.

² This effect of a shock has to be distinguished from that of renewables structurally being must-run technologies because of the very low marginal costs serving the users with a lower price in the absence of shocks; this could be captured by endowment theory.

³ Janda, Malek, and Recka (2017) describe the problem intuitively and with data for certain weeks and with a model for given capacity choosing production of plants and demand of users.

⁴ The moment condition for first differences, E[z_is dε_it] = 0 for s ≤ t, is replaced by E[z_is(ε_it −ε¯_i^F)] = 0 for s ≤ t, where ε¯_i^F is the average over all future residuals.

⁵ See Greene (2003), p. 291, formula 13–17, with x including the lagged dependent variable.

⁶ 2SLS instrument weighting matrix; Cross-section weights (PCSE) standard errors & covariance (d.f. corrected);

Instrument specification: (LOG(1+ ECIM) with lag −2), LOG(GDPR), LOG(1+ RNR)-LOG(1+ RNR(−1)), LOG(1+ RNP), time dummies.

⁷ See Greene (2003), p. 291, formula 13–17, with x including the lagged dependent variable.

⁸ 2SLS instrument weighting matrix; Cross-section weights (PCSE) standard errors & covariance (d.f. corrected);

Instrument specification: (LOG(1+ ECEX),with lags −2 to −4), LOG(GDPR), LOG(1+ RNR), LOG(1+ RNP), LOG(1+ RNR(−1)), LOG(1+ RNP(−1)).

⁹ Differences in stochastic draws of farmers in the two countries are a source of short run comparative advantage in Newbery and Stiglitz (1984) if expected values are equal.